Scrapy 1.0 documentation

This documentation contains everything you need to know about Scrapy.

Getting help

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First steps

Scrapy at a glance

Scrapy is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival.

Even though Scrapy was originally designed for web scraping, it can also be used to extract data using APIs (such as Amazon Associates Web Services) or as a general purpose web crawler.

Walk-through of an example spider

In order to show you what Scrapy brings to the table, we’ll walk you through an example of a Scrapy Spider using the simplest way to run a spider.

So, here’s the code for a spider that follows the links to the top voted questions on StackOverflow and scrapes some data from each page:

import scrapy


class StackOverflowSpider(scrapy.Spider):
    name = 'stackoverflow'
    start_urls = ['http://stackoverflow.com/questions?sort=votes']

    def parse(self, response):
        for href in response.css('.question-summary h3 a::attr(href)'):
            full_url = response.urljoin(href.extract())
            yield scrapy.Request(full_url, callback=self.parse_question)

    def parse_question(self, response):
        yield {
            'title': response.css('h1 a::text').extract()[0],
            'votes': response.css('.question .vote-count-post::text').extract()[0],
            'body': response.css('.question .post-text').extract()[0],
            'tags': response.css('.question .post-tag::text').extract(),
            'link': response.url,
        }

Put this in a file, name it to something like stackoverflow_spider.py and run the spider using the runspider command:

scrapy runspider stackoverflow_spider.py -o top-stackoverflow-questions.json

When this finishes you will have in the top-stackoverflow-questions.json file a list of the most upvoted questions in StackOverflow in JSON format, containing the title, link, number of upvotes, a list of the tags and the question content in HTML, looking like this (reformatted for easier reading):

[{
    "body": "... LONG HTML HERE ...",
    "link": "http://stackoverflow.com/questions/11227809/why-is-processing-a-sorted-array-faster-than-an-unsorted-array",
    "tags": ["java", "c++", "performance", "optimization"],
    "title": "Why is processing a sorted array faster than an unsorted array?",
    "votes": "9924"
},
{
    "body": "... LONG HTML HERE ...",
    "link": "http://stackoverflow.com/questions/1260748/how-do-i-remove-a-git-submodule",
    "tags": ["git", "git-submodules"],
    "title": "How do I remove a Git submodule?",
    "votes": "1764"
},
...]
What just happened?

When you ran the command scrapy runspider somefile.py, Scrapy looked for a Spider definition inside it and ran it through its crawler engine.

The crawl started by making requests to the URLs defined in the start_urls attribute (in this case, only the URL for StackOverflow top questions page) and called the default callback method parse, passing the response object as an argument. In the parse callback we extract the links to the question pages using a CSS Selector with a custom extension that allows to get the value for an attribute. Then we yield a few more requests to be sent, registering the method parse_question as the callback to be called for each of them as they finish.

Here you notice one of the main advantages about Scrapy: requests are scheduled and processed asynchronously. This means that Scrapy doesn’t need to wait for a request to be finished and processed, it can send another request or do other things in the meantime. This also means that other requests can keep going even if some request fails or an error happens while handling it.

While this enables you to do very fast crawls (sending multiple concurrent requests at the same time, in a fault-tolerant way) Scrapy also gives you control over the politeness of the crawl through a few settings. You can do things like setting a download delay between each request, limiting amount of concurrent requests per domain or per IP, and even using an auto-throttling extension that tries to figure out these automatically.

Finally, the parse_question callback scrapes the question data for each page yielding a dict, which Scrapy then collects and writes to a JSON file as requested in the command line.

Note

This is using feed exports to generate the JSON file, you can easily change the export format (XML or CSV, for example) or the storage backend (FTP or Amazon S3, for example). You can also write an item pipeline to store the items in a database.

What else?

You’ve seen how to extract and store items from a website using Scrapy, but this is just the surface. Scrapy provides a lot of powerful features for making scraping easy and efficient, such as:

  • Built-in support for selecting and extracting data from HTML/XML sources using extended CSS selectors and XPath expressions, with helper methods to extract using regular expressions.
  • An interactive shell console (IPython aware) for trying out the CSS and XPath expressions to scrape data, very useful when writing or debugging your spiders.
  • Built-in support for generating feed exports in multiple formats (JSON, CSV, XML) and storing them in multiple backends (FTP, S3, local filesystem)
  • Robust encoding support and auto-detection, for dealing with foreign, non-standard and broken encoding declarations.
  • Strong extensibility support, allowing you to plug in your own functionality using signals and a well-defined API (middlewares, extensions, and pipelines).
  • Wide range of built-in extensions and middlewares for handling:
    • cookies and session handling
    • HTTP features like compression, authentication, caching
    • user-agent spoofing
    • robots.txt
    • crawl depth restriction
    • and more
  • A Telnet console for hooking into a Python console running inside your Scrapy process, to introspect and debug your crawler
  • Plus other goodies like reusable spiders to crawl sites from Sitemaps and XML/CSV feeds, a media pipeline for automatically downloading images (or any other media) associated with the scraped items, a caching DNS resolver, and much more!

What’s next?

The next steps for you are to install Scrapy, follow through the tutorial to learn how to organize your code in Scrapy projects and join the community. Thanks for your interest!

Installation guide

Installing Scrapy

Note

Check Platform specific installation notes first.

The installation steps assume that you have the following things installed:

  • Python 2.7
  • pip and setuptools Python packages. Nowadays pip requires and installs setuptools if not installed. Python 2.7.9 and later include pip by default, so you may have it already.
  • lxml. Most Linux distributions ships prepackaged versions of lxml. Otherwise refer to http://lxml.de/installation.html
  • OpenSSL. This comes preinstalled in all operating systems, except Windows where the Python installer ships it bundled.

You can install Scrapy using pip (which is the canonical way to install Python packages). To install using pip run:

pip install Scrapy

Platform specific installation notes

Anaconda

Note

For Windows users, or if you have issues installing through pip, this is the recommended way to install Scrapy.

If you already have installed Anaconda or Miniconda, the company Scrapinghub maintains official conda packages for Linux, Windows and OS X.

To install Scrapy using conda, run:

conda install -c scrapinghub scrapy
Windows

  • Install Python 2.7 from https://www.python.org/downloads/

    You need to adjust PATH environment variable to include paths to the Python executable and additional scripts. The following paths need to be added to PATH:

    C:\Python27\;C:\Python27\Scripts\;

    To update the PATH open a Command prompt and run:

    c:\python27\python.exe c:\python27\tools\scripts\win_add2path.py

    Close the command prompt window and reopen it so changes take effect, run the following command and check it shows the expected Python version:

    python --version

  • Install pywin32 from http://sourceforge.net/projects/pywin32/

    Be sure you download the architecture (win32 or amd64) that matches your system

  • (Only required for Python<2.7.9) Install pip from https://pip.pypa.io/en/latest/installing.html

    Now open a Command prompt to check pip is installed correctly:

    pip --version

  • At this point Python 2.7 and pip package manager must be working, let’s install Scrapy:

    pip install Scrapy
Ubuntu 9.10 or above

Don’t use the python-scrapy package provided by Ubuntu, they are typically too old and slow to catch up with latest Scrapy.

Instead, use the official Ubuntu Packages, which already solve all dependencies for you and are continuously updated with the latest bug fixes.

If you prefer to build the python dependencies locally instead of relying on system packages you’ll need to install their required non-python dependencies first:

sudo apt-get install python-dev python-pip libxml2-dev libxslt1-dev zlib1g-dev libffi-dev libssl-dev

You can install Scrapy with pip after that:

pip install Scrapy

Note

The same non-python dependencies can be used to install Scrapy in Debian Wheezy (7.0) and above.

Archlinux

You can follow the generic instructions or install Scrapy from AUR Scrapy package:

yaourt -S scrapy
Mac OS X

Building Scrapy’s dependencies requires the presence of a C compiler and development headers. On OS X this is typically provided by Apple’s Xcode development tools. To install the Xcode command line tools open a terminal window and run:

xcode-select --install

There’s a known issue that prevents pip from updating system packages. This has to be addressed to successfully install Scrapy and its dependencies. Here are some proposed solutions:

  • (Recommended) Don’t use system python, install a new, updated version that doesn’t conflict with the rest of your system. Here’s how to do it using the homebrew package manager:

    • Install homebrew following the instructions in http://brew.sh/

    • Update your PATH variable to state that homebrew packages should be used before system packages (Change .bashrc to .zshrc accordantly if you’re using zsh as default shell):

      echo "export PATH=/usr/local/bin:/usr/local/sbin:$PATH" >> ~/.bashrc

    • Reload .bashrc to ensure the changes have taken place:

      source ~/.bashrc

    • Install python:

      brew install python

    • Latest versions of python have pip bundled with them so you won’t need to install it separately. If this is not the case, upgrade python:

      brew update; brew upgrade python

  • (Optional) Install Scrapy inside an isolated python environment.

    This method is a workaround for the above OS X issue, but it’s an overall good practice for managing dependencies and can complement the first method.

    virtualenv is a tool you can use to create virtual environments in python. We recommended reading a tutorial like http://docs.python-guide.org/en/latest/dev/virtualenvs/ to get started.

After any of these workarounds you should be able to install Scrapy:

pip install Scrapy

Scrapy Tutorial

In this tutorial, we’ll assume that Scrapy is already installed on your system. If that’s not the case, see Installation guide.

We are going to use Open directory project (dmoz) as our example domain to scrape.

This tutorial will walk you through these tasks:

  1. Creating a new Scrapy project
  2. Defining the Items you will extract
  3. Writing a spider to crawl a site and extract Items
  4. Writing an Item Pipeline to store the extracted Items

Scrapy is written in Python. If you’re new to the language you might want to start by getting an idea of what the language is like, to get the most out of Scrapy. If you’re already familiar with other languages, and want to learn Python quickly, we recommend Learn Python The Hard Way. If you’re new to programming and want to start with Python, take a look at this list of Python resources for non-programmers.

Creating a project

Before you start scraping, you will have to set up a new Scrapy project. Enter a directory where you’d like to store your code and run:

scrapy startproject tutorial

This will create a tutorial directory with the following contents:

tutorial/
    scrapy.cfg            # deploy configuration file

    tutorial/             # project's Python module, you'll import your code from here
        __init__.py

        items.py          # project items file

        pipelines.py      # project pipelines file

        settings.py       # project settings file

        spiders/          # a directory where you'll later put your spiders
            __init__.py
            ...

Defining our Item

Items are containers that will be loaded with the scraped data; they work like simple Python dicts. While you can use plain Python dicts with Scrapy, Items provide additional protection against populating undeclared fields, preventing typos. They can also be used with Item Loaders, a mechanism with helpers to conveniently populate Items.

They are declared by creating a scrapy.Item class and defining its attributes as scrapy.Field objects, much like in an ORM (don’t worry if you’re not familiar with ORMs, you will see that this is an easy task).

We begin by modeling the item that we will use to hold the site’s data obtained from dmoz.org. As we want to capture the name, url and description of the sites, we define fields for each of these three attributes. To do that, we edit items.py, found in the tutorial directory. Our Item class looks like this:

import scrapy

class DmozItem(scrapy.Item):
    title = scrapy.Field()
    link = scrapy.Field()
    desc = scrapy.Field()

This may seem complicated at first, but defining an item class allows you to use other handy components and helpers within Scrapy.

Our first Spider

Spiders are classes that you define and Scrapy uses to scrape information from a domain (or group of domains).

They define an initial list of URLs to download, how to follow links, and how to parse the contents of pages to extract items.

To create a Spider, you must subclass scrapy.Spider and define some attributes:

  • name: identifies the Spider. It must be unique, that is, you can’t set the same name for different Spiders.

  • start_urls: a list of URLs where the Spider will begin to crawl from. The first pages downloaded will be those listed here. The subsequent URLs will be generated successively from data contained in the start URLs.

  • parse(): a method of the spider, which will be called with the downloaded Response object of each start URL. The response is passed to the method as the first and only argument.

    This method is responsible for parsing the response data and extracting scraped data (as scraped items) and more URLs to follow.

    The parse() method is in charge of processing the response and returning scraped data (as Item objects) and more URLs to follow (as Request objects).

This is the code for our first Spider; save it in a file named dmoz_spider.py under the tutorial/spiders directory:

import scrapy

class DmozSpider(scrapy.Spider):
    name = "dmoz"
    allowed_domains = ["dmoz.org"]
    start_urls = [
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
    ]

    def parse(self, response):
        filename = response.url.split("/")[-2] + '.html'
        with open(filename, 'wb') as f:
            f.write(response.body)
Crawling

To put our spider to work, go to the project’s top level directory and run:

scrapy crawl dmoz

This command runs the spider with name dmoz that we’ve just added, that will send some requests for the dmoz.org domain. You will get an output similar to this:

2014-01-23 18:13:07-0400 [scrapy] INFO: Scrapy started (bot: tutorial)
2014-01-23 18:13:07-0400 [scrapy] INFO: Optional features available: ...
2014-01-23 18:13:07-0400 [scrapy] INFO: Overridden settings: {}
2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled extensions: ...
2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled downloader middlewares: ...
2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled spider middlewares: ...
2014-01-23 18:13:07-0400 [scrapy] INFO: Enabled item pipelines: ...
2014-01-23 18:13:07-0400 [scrapy] INFO: Spider opened
2014-01-23 18:13:08-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/> (referer: None)
2014-01-23 18:13:09-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> (referer: None)
2014-01-23 18:13:09-0400 [scrapy] INFO: Closing spider (finished)

Note

At the end you can see a log line for each URL defined in start_urls. Because these URLs are the starting ones, they have no referrers, which is shown at the end of the log line, where it says (referer: None).

Now, check the files in the current directory. You should notice two new files have been created: Books.html and Resources.html, with the content for the respective URLs, as our parse method instructs.

What just happened under the hood?

Scrapy creates scrapy.Request objects for each URL in the start_urls attribute of the Spider, and assigns them the parse method of the spider as their callback function.

These Requests are scheduled, then executed, and scrapy.http.Response objects are returned and then fed back to the spider, through the parse() method.

Extracting Items
Introduction to Selectors

There are several ways to extract data from web pages. Scrapy uses a mechanism based on XPath or CSS expressions called Scrapy Selectors. For more information about selectors and other extraction mechanisms see the Selectors documentation.

Here are some examples of XPath expressions and their meanings:

  • /html/head/title: selects the <title> element, inside the <head> element of an HTML document
  • /html/head/title/text(): selects the text inside the aforementioned <title> element.
  • //td: selects all the <td> elements
  • //div[@class="mine"]: selects all div elements which contain an attribute class="mine"

These are just a couple of simple examples of what you can do with XPath, but XPath expressions are indeed much more powerful. To learn more about XPath, we recommend this tutorial to learn XPath through examples, and this tutorial to learn “how to think in XPath”.

Note

CSS vs XPath: you can go a long way extracting data from web pages using only CSS selectors. However, XPath offers more power because besides navigating the structure, it can also look at the content: you’re able to select things like: the link that contains the text ‘Next Page’. Because of this, we encourage you to learn about XPath even if you already know how to construct CSS selectors.

For working with CSS and XPath expressions, Scrapy provides Selector class and convenient shortcuts to avoid instantiating selectors yourself every time you need to select something from a response.

You can see selectors as objects that represent nodes in the document structure. So, the first instantiated selectors are associated with the root node, or the entire document.

Selectors have four basic methods (click on the method to see the complete API documentation):

  • xpath(): returns a list of selectors, each of which represents the nodes selected by the xpath expression given as argument.
  • css(): returns a list of selectors, each of which represents the nodes selected by the CSS expression given as argument.
  • extract(): returns a unicode string with the selected data.
  • re(): returns a list of unicode strings extracted by applying the regular expression given as argument.
Trying Selectors in the Shell

To illustrate the use of Selectors we’re going to use the built-in Scrapy shell, which also requires IPython (an extended Python console) installed on your system.

To start a shell, you must go to the project’s top level directory and run:

scrapy shell "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/"

Note

Remember to always enclose urls in quotes when running Scrapy shell from command-line, otherwise urls containing arguments (ie. & character) will not work.

This is what the shell looks like:

[ ... Scrapy log here ... ]

2014-01-23 17:11:42-0400 [scrapy] DEBUG: Crawled (200) <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/> (referer: None)
[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x3636b50>
[s]   item       {}
[s]   request    <GET http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
[s]   response   <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
[s]   settings   <scrapy.settings.Settings object at 0x3fadc50>
[s]   spider     <Spider 'default' at 0x3cebf50>
[s] Useful shortcuts:
[s]   shelp()           Shell help (print this help)
[s]   fetch(req_or_url) Fetch request (or URL) and update local objects
[s]   view(response)    View response in a browser

In [1]:

After the shell loads, you will have the response fetched in a local response variable, so if you type response.body you will see the body of the response, or you can type response.headers to see its headers.

More importantly response has a selector attribute which is an instance of Selector class, instantiated with this particular response. You can run queries on response by calling response.selector.xpath() or response.selector.css(). There are also some convenience shortcuts like response.xpath() or response.css() which map directly to response.selector.xpath() and response.selector.css().

So let’s try it:

In [1]: response.xpath('//title')
Out[1]: [<Selector xpath='//title' data=u'<title>Open Directory - Computers: Progr'>]

In [2]: response.xpath('//title').extract()
Out[2]: [u'<title>Open Directory - Computers: Programming: Languages: Python: Books</title>']

In [3]: response.xpath('//title/text()')
Out[3]: [<Selector xpath='//title/text()' data=u'Open Directory - Computers: Programming:'>]

In [4]: response.xpath('//title/text()').extract()
Out[4]: [u'Open Directory - Computers: Programming: Languages: Python: Books']

In [5]: response.xpath('//title/text()').re('(\w+):')
Out[5]: [u'Computers', u'Programming', u'Languages', u'Python']
Extracting the data

Now, let’s try to extract some real information from those pages.

You could type response.body in the console, and inspect the source code to figure out the XPaths you need to use. However, inspecting the raw HTML code there could become a very tedious task. To make it easier, you can use Firefox Developer Tools or some Firefox extensions like Firebug. For more information see Using Firebug for scraping and Using Firefox for scraping.

After inspecting the page source, you’ll find that the web site’s information is inside a <ul> element, in fact the second <ul> element.

So we can select each <li> element belonging to the site’s list with this code:

response.xpath('//ul/li')

And from them, the site’s descriptions:

response.xpath('//ul/li/text()').extract()

The site’s titles:

response.xpath('//ul/li/a/text()').extract()

And the site’s links:

response.xpath('//ul/li/a/@href').extract()

As we’ve said before, each .xpath() call returns a list of selectors, so we can concatenate further .xpath() calls to dig deeper into a node. We are going to use that property here, so:

for sel in response.xpath('//ul/li'):
    title = sel.xpath('a/text()').extract()
    link = sel.xpath('a/@href').extract()
    desc = sel.xpath('text()').extract()
    print title, link, desc

Note

For a more detailed description of using nested selectors, see Nesting selectors and Working with relative XPaths in the Selectors documentation

Let’s add this code to our spider:

import scrapy

class DmozSpider(scrapy.Spider):
    name = "dmoz"
    allowed_domains = ["dmoz.org"]
    start_urls = [
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
    ]

    def parse(self, response):
        for sel in response.xpath('//ul/li'):
            title = sel.xpath('a/text()').extract()
            link = sel.xpath('a/@href').extract()
            desc = sel.xpath('text()').extract()
            print title, link, desc

Now try crawling dmoz.org again and you’ll see sites being printed in your output. Run:

scrapy crawl dmoz
Using our item

Item objects are custom Python dicts; you can access the values of their fields (attributes of the class we defined earlier) using the standard dict syntax like:

>>> item = DmozItem()
>>> item['title'] = 'Example title'
>>> item['title']
'Example title'

So, in order to return the data we’ve scraped so far, the final code for our Spider would be like this:

import scrapy

from tutorial.items import DmozItem

class DmozSpider(scrapy.Spider):
    name = "dmoz"
    allowed_domains = ["dmoz.org"]
    start_urls = [
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Books/",
        "http://www.dmoz.org/Computers/Programming/Languages/Python/Resources/"
    ]

    def parse(self, response):
        for sel in response.xpath('//ul/li'):
            item = DmozItem()
            item['title'] = sel.xpath('a/text()').extract()
            item['link'] = sel.xpath('a/@href').extract()
            item['desc'] = sel.xpath('text()').extract()
            yield item

Note

You can find a fully-functional variant of this spider in the dirbot project available at https://github.com/scrapy/dirbot

Now crawling dmoz.org yields DmozItem objects:

[scrapy] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
     {'desc': [u' - By David Mertz; Addison Wesley. Book in progress, full text, ASCII format. Asks for feedback. [author website, Gnosis Software, Inc.\n],
      'link': [u'http://gnosis.cx/TPiP/'],
      'title': [u'Text Processing in Python']}
[scrapy] DEBUG: Scraped from <200 http://www.dmoz.org/Computers/Programming/Languages/Python/Books/>
     {'desc': [u' - By Sean McGrath; Prentice Hall PTR, 2000, ISBN 0130211192, has CD-ROM. Methods to build XML applications fast, Python tutorial, DOM and SAX, new Pyxie open source XML processing library. [Prentice Hall PTR]\n'],
      'link': [u'http://www.informit.com/store/product.aspx?isbn=0130211192'],
      'title': [u'XML Processing with Python']}

Storing the scraped data

The simplest way to store the scraped data is by using Feed exports, with the following command:

scrapy crawl dmoz -o items.json

That will generate an items.json file containing all scraped items, serialized in JSON.

In small projects (like the one in this tutorial), that should be enough. However, if you want to perform more complex things with the scraped items, you can write an Item Pipeline. As with Items, a placeholder file for Item Pipelines has been set up for you when the project is created, in tutorial/pipelines.py. Though you don’t need to implement any item pipelines if you just want to store the scraped items.

Next steps

This tutorial covered only the basics of Scrapy, but there’s a lot of other features not mentioned here. Check the What else? section in Scrapy at a glance chapter for a quick overview of the most important ones.

Then, we recommend you continue by playing with an example project (see Examples), and then continue with the section Basic concepts.

Examples

The best way to learn is with examples, and Scrapy is no exception. For this reason, there is an example Scrapy project named dirbot, that you can use to play and learn more about Scrapy. It contains the dmoz spider described in the tutorial.

This dirbot project is available at: https://github.com/scrapy/dirbot

It contains a README file with a detailed description of the project contents.

If you’re familiar with git, you can checkout the code. Otherwise you can download a tarball or zip file of the project by clicking on Downloads.

The scrapy tag on Snipplr is used for sharing code snippets such as spiders, middlewares, extensions, or scripts. Feel free (and encouraged!) to share any code there.

Scrapy at a glance
Understand what Scrapy is and how it can help you.
Installation guide
Get Scrapy installed on your computer.
Scrapy Tutorial
Write your first Scrapy project.
Examples
Learn more by playing with a pre-made Scrapy project.

Basic concepts

Command line tool

New in version 0.10.

Scrapy is controlled through the scrapy command-line tool, to be referred here as the “Scrapy tool” to differentiate it from the sub-commands, which we just call “commands” or “Scrapy commands”.

The Scrapy tool provides several commands, for multiple purposes, and each one accepts a different set of arguments and options.

(The scrapy deploy command has been removed in 1.0 in favor of the standalone scrapyd-deploy. See Deploying your project.)

Configuration settings

Scrapy will look for configuration parameters in ini-style scrapy.cfg files in standard locations:

  1. /etc/scrapy.cfg or c:\scrapy\scrapy.cfg (system-wide),
  2. ~/.config/scrapy.cfg ($XDG_CONFIG_HOME) and ~/.scrapy.cfg ($HOME) for global (user-wide) settings, and
  3. scrapy.cfg inside a scrapy project’s root (see next section).

Settings from these files are merged in the listed order of preference: user-defined values have higher priority than system-wide defaults and project-wide settings will override all others, when defined.

Scrapy also understands, and can be configured through, a number of environment variables. Currently these are:

Default structure of Scrapy projects

Before delving into the command-line tool and its sub-commands, let’s first understand the directory structure of a Scrapy project.

Though it can be modified, all Scrapy projects have the same file structure by default, similar to this:

scrapy.cfg
myproject/
    __init__.py
    items.py
    pipelines.py
    settings.py
    spiders/
        __init__.py
        spider1.py
        spider2.py
        ...

The directory where the scrapy.cfg file resides is known as the project root directory. That file contains the name of the python module that defines the project settings. Here is an example:

[settings]
default = myproject.settings

Using the scrapy tool

You can start by running the Scrapy tool with no arguments and it will print some usage help and the available commands:

Scrapy X.Y - no active project

Usage:
  scrapy <command> [options] [args]

Available commands:
  crawl         Run a spider
  fetch         Fetch a URL using the Scrapy downloader
[...]

The first line will print the currently active project if you’re inside a Scrapy project. In this example it was run from outside a project. If run from inside a project it would have printed something like this:

Scrapy X.Y - project: myproject

Usage:
  scrapy <command> [options] [args]

[...]
Creating projects

The first thing you typically do with the scrapy tool is create your Scrapy project:

scrapy startproject myproject

That will create a Scrapy project under the myproject directory.

Next, you go inside the new project directory:

cd myproject

And you’re ready to use the scrapy command to manage and control your project from there.

Controlling projects

You use the scrapy tool from inside your projects to control and manage them.

For example, to create a new spider:

scrapy genspider mydomain mydomain.com

Some Scrapy commands (like crawl) must be run from inside a Scrapy project. See the commands reference below for more information on which commands must be run from inside projects, and which not.

Also keep in mind that some commands may have slightly different behaviours when running them from inside projects. For example, the fetch command will use spider-overridden behaviours (such as the user_agent attribute to override the user-agent) if the url being fetched is associated with some specific spider. This is intentional, as the fetch command is meant to be used to check how spiders are downloading pages.

Available tool commands

This section contains a list of the available built-in commands with a description and some usage examples. Remember, you can always get more info about each command by running:

scrapy <command> -h

And you can see all available commands with:

scrapy -h

There are two kinds of commands, those that only work from inside a Scrapy project (Project-specific commands) and those that also work without an active Scrapy project (Global commands), though they may behave slightly different when running from inside a project (as they would use the project overridden settings).

Global commands:

Project-only commands:

startproject
  • Syntax: scrapy startproject <project_name>
  • Requires project: no

Creates a new Scrapy project named project_name, under the project_name directory.

Usage example:

$ scrapy startproject myproject
genspider
  • Syntax: scrapy genspider [-t template] <name> <domain>
  • Requires project: yes

Create a new spider in the current project.

This is just a convenience shortcut command for creating spiders based on pre-defined templates, but certainly not the only way to create spiders. You can just create the spider source code files yourself, instead of using this command.

Usage example:

$ scrapy genspider -l
Available templates:
  basic
  crawl
  csvfeed
  xmlfeed

$ scrapy genspider -d basic
import scrapy

class $classname(scrapy.Spider):
    name = "$name"
    allowed_domains = ["$domain"]
    start_urls = (
        'http://www.$domain/',
        )

    def parse(self, response):
        pass

$ scrapy genspider -t basic example example.com
Created spider 'example' using template 'basic' in module:
  mybot.spiders.example
crawl
  • Syntax: scrapy crawl <spider>
  • Requires project: yes

Start crawling using a spider.

Usage examples:

$ scrapy crawl myspider
[ ... myspider starts crawling ... ]
check
  • Syntax: scrapy check [-l] <spider>
  • Requires project: yes

Run contract checks.

Usage examples:

$ scrapy check -l
first_spider
  * parse
  * parse_item
second_spider
  * parse
  * parse_item

$ scrapy check
[FAILED] first_spider:parse_item
>>> 'RetailPricex' field is missing

[FAILED] first_spider:parse
>>> Returned 92 requests, expected 0..4
list
  • Syntax: scrapy list
  • Requires project: yes

List all available spiders in the current project. The output is one spider per line.

Usage example:

$ scrapy list
spider1
spider2
edit
  • Syntax: scrapy edit <spider>
  • Requires project: yes

Edit the given spider using the editor defined in the EDITOR setting.

This command is provided only as a convenience shortcut for the most common case, the developer is of course free to choose any tool or IDE to write and debug his spiders.

Usage example:

$ scrapy edit spider1
fetch
  • Syntax: scrapy fetch <url>
  • Requires project: no

Downloads the given URL using the Scrapy downloader and writes the contents to standard output.

The interesting thing about this command is that it fetches the page how the spider would download it. For example, if the spider has a USER_AGENT attribute which overrides the User Agent, it will use that one.

So this command can be used to “see” how your spider would fetch a certain page.

If used outside a project, no particular per-spider behaviour would be applied and it will just use the default Scrapy downloader settings.

Usage examples:

$ scrapy fetch --nolog http://www.example.com/some/page.html
[ ... html content here ... ]

$ scrapy fetch --nolog --headers http://www.example.com/
{'Accept-Ranges': ['bytes'],
 'Age': ['1263   '],
 'Connection': ['close     '],
 'Content-Length': ['596'],
 'Content-Type': ['text/html; charset=UTF-8'],
 'Date': ['Wed, 18 Aug 2010 23:59:46 GMT'],
 'Etag': ['"573c1-254-48c9c87349680"'],
 'Last-Modified': ['Fri, 30 Jul 2010 15:30:18 GMT'],
 'Server': ['Apache/2.2.3 (CentOS)']}
view
  • Syntax: scrapy view <url>
  • Requires project: no

Opens the given URL in a browser, as your Scrapy spider would “see” it. Sometimes spiders see pages differently from regular users, so this can be used to check what the spider “sees” and confirm it’s what you expect.

Usage example:

$ scrapy view http://www.example.com/some/page.html
[ ... browser starts ... ]
shell
  • Syntax: scrapy shell [url]
  • Requires project: no

Starts the Scrapy shell for the given URL (if given) or empty if no URL is given. See Scrapy shell for more info.

Usage example:

$ scrapy shell http://www.example.com/some/page.html
[ ... scrapy shell starts ... ]
parse
  • Syntax: scrapy parse <url> [options]
  • Requires project: yes

Fetches the given URL and parses it with the spider that handles it, using the method passed with the --callback option, or parse if not given.

Supported options:

  • --spider=SPIDER: bypass spider autodetection and force use of specific spider
  • --a NAME=VALUE: set spider argument (may be repeated)
  • --callback or -c: spider method to use as callback for parsing the response
  • --pipelines: process items through pipelines
  • --rules or -r: use CrawlSpider rules to discover the callback (i.e. spider method) to use for parsing the response
  • --noitems: don’t show scraped items
  • --nolinks: don’t show extracted links
  • --nocolour: avoid using pygments to colorize the output
  • --depth or -d: depth level for which the requests should be followed recursively (default: 1)
  • --verbose or -v: display information for each depth level

Usage example:

$ scrapy parse http://www.example.com/ -c parse_item
[ ... scrapy log lines crawling example.com spider ... ]

>>> STATUS DEPTH LEVEL 1 <<<
# Scraped Items  ------------------------------------------------------------
[{'name': u'Example item',
 'category': u'Furniture',
 'length': u'12 cm'}]

# Requests  -----------------------------------------------------------------
[]
settings
  • Syntax: scrapy settings [options]
  • Requires project: no

Get the value of a Scrapy setting.

If used inside a project it’ll show the project setting value, otherwise it’ll show the default Scrapy value for that setting.

Example usage:

$ scrapy settings --get BOT_NAME
scrapybot
$ scrapy settings --get DOWNLOAD_DELAY
0
runspider
  • Syntax: scrapy runspider <spider_file.py>
  • Requires project: no

Run a spider self-contained in a Python file, without having to create a project.

Example usage:

$ scrapy runspider myspider.py
[ ... spider starts crawling ... ]
version
  • Syntax: scrapy version [-v]
  • Requires project: no

Prints the Scrapy version. If used with -v it also prints Python, Twisted and Platform info, which is useful for bug reports.

bench

New in version 0.17.

  • Syntax: scrapy bench
  • Requires project: no

Run a quick benchmark test. Benchmarking.

Custom project commands

You can also add your custom project commands by using the COMMANDS_MODULE setting. See the Scrapy commands in scrapy/commands for examples on how to implement your commands.

COMMANDS_MODULE

Default: '' (empty string)

A module to use for looking up custom Scrapy commands. This is used to add custom commands for your Scrapy project.

Example:

COMMANDS_MODULE = 'mybot.commands'
Register commands via setup.py entry points

Note

This is an experimental feature, use with caution.

You can also add Scrapy commands from an external library by adding a scrapy.commands section in the entry points of the library setup.py file.

The following example adds my_command command:

from setuptools import setup, find_packages

setup(name='scrapy-mymodule',
  entry_points={
    'scrapy.commands': [
      'my_command=my_scrapy_module.commands:MyCommand',
    ],
  },
 )

Spiders

Spiders are classes which define how a certain site (or a group of sites) will be scraped, including how to perform the crawl (i.e. follow links) and how to extract structured data from their pages (i.e. scraping items). In other words, Spiders are the place where you define the custom behaviour for crawling and parsing pages for a particular site (or, in some cases, a group of sites).

For spiders, the scraping cycle goes through something like this:

  1. You start by generating the initial Requests to crawl the first URLs, and specify a callback function to be called with the response downloaded from those requests.

    The first requests to perform are obtained by calling the start_requests() method which (by default) generates Request for the URLs specified in the start_urls and the parse method as callback function for the Requests.

  2. In the callback function, you parse the response (web page) and return either dicts with extracted data, Item objects, Request objects, or an iterable of these objects. Those Requests will also contain a callback (maybe the same) and will then be downloaded by Scrapy and then their response handled by the specified callback.

  3. In callback functions, you parse the page contents, typically using Selectors (but you can also use BeautifulSoup, lxml or whatever mechanism you prefer) and generate items with the parsed data.

  4. Finally, the items returned from the spider will be typically persisted to a database (in some Item Pipeline) or written to a file using Feed exports.

Even though this cycle applies (more or less) to any kind of spider, there are different kinds of default spiders bundled into Scrapy for different purposes. We will talk about those types here.

scrapy.Spider

class scrapy.spiders.Spider

This is the simplest spider, and the one from which every other spider must inherit (including spiders that come bundled with Scrapy, as well as spiders that you write yourself). It doesn’t provide any special functionality. It just provides a default start_requests() implementation which sends requests from the start_urls spider attribute and calls the spider’s method parse for each of the resulting responses.

name

A string which defines the name for this spider. The spider name is how the spider is located (and instantiated) by Scrapy, so it must be unique. However, nothing prevents you from instantiating more than one instance of the same spider. This is the most important spider attribute and it’s required.

If the spider scrapes a single domain, a common practice is to name the spider after the domain, with or without the TLD. So, for example, a spider that crawls mywebsite.com would often be called mywebsite.

allowed_domains

An optional list of strings containing domains that this spider is allowed to crawl. Requests for URLs not belonging to the domain names specified in this list won’t be followed if OffsiteMiddleware is enabled.

start_urls

A list of URLs where the spider will begin to crawl from, when no particular URLs are specified. So, the first pages downloaded will be those listed here. The subsequent URLs will be generated successively from data contained in the start URLs.

custom_settings

A dictionary of settings that will be overridden from the project wide configuration when running this spider. It must be defined as a class attribute since the settings are updated before instantiation.

For a list of available built-in settings see: Built-in settings reference.

crawler

This attribute is set by the from_crawler() class method after initializating the class, and links to the Crawler object to which this spider instance is bound.

Crawlers encapsulate a lot of components in the project for their single entry access (such as extensions, middlewares, signals managers, etc). See Crawler API to know more about them.

settings

Configuration for running this spider. This is a Settings instance, see the Settings topic for a detailed introduction on this subject.

logger

Python logger created with the Spider’s name. You can use it to send log messages through it as described on Logging from Spiders.

from_crawler(crawler, *args, **kwargs)

This is the class method used by Scrapy to create your spiders.

You probably won’t need to override this directly because the default implementation acts as a proxy to the __init__() method, calling it with the given arguments args and named arguments kwargs.

Nonetheless, this method sets the crawler and settings attributes in the new instance so they can be accessed later inside the spider’s code.

Parameters:
  • crawler (Crawler instance) – crawler to which the spider will be bound
  • args (list) – arguments passed to the __init__() method
  • kwargs (dict) – keyword arguments passed to the __init__() method
start_requests()

This method must return an iterable with the first Requests to crawl for this spider.

This is the method called by Scrapy when the spider is opened for scraping when no particular URLs are specified. If particular URLs are specified, the make_requests_from_url() is used instead to create the Requests. This method is also called only once from Scrapy, so it’s safe to implement it as a generator.

The default implementation uses make_requests_from_url() to generate Requests for each url in start_urls.

If you want to change the Requests used to start scraping a domain, this is the method to override. For example, if you need to start by logging in using a POST request, you could do:

class MySpider(scrapy.Spider):
    name = 'myspider'

    def start_requests(self):
        return [scrapy.FormRequest("http://www.example.com/login",
                                   formdata={'user': 'john', 'pass': 'secret'},
                                   callback=self.logged_in)]

    def logged_in(self, response):
        # here you would extract links to follow and return Requests for
        # each of them, with another callback
        pass
make_requests_from_url(url)

A method that receives a URL and returns a Request object (or a list of Request objects) to scrape. This method is used to construct the initial requests in the start_requests() method, and is typically used to convert urls to requests.

Unless overridden, this method returns Requests with the parse() method as their callback function, and with dont_filter parameter enabled (see Request class for more info).

parse(response)

This is the default callback used by Scrapy to process downloaded responses, when their requests don’t specify a callback.

The parse method is in charge of processing the response and returning scraped data and/or more URLs to follow. Other Requests callbacks have the same requirements as the Spider class.

This method, as well as any other Request callback, must return an iterable of Request and/or dicts or Item objects.

Parameters:response (Response) – the response to parse
log(message[, level, component])

Wrapper that sends a log message through the Spider’s logger, kept for backwards compatibility. For more information see Logging from Spiders.

closed(reason)

Called when the spider closes. This method provides a shortcut to signals.connect() for the spider_closed signal.

Let’s see an example:

import scrapy


class MySpider(scrapy.Spider):
    name = 'example.com'
    allowed_domains = ['example.com']
    start_urls = [
        'http://www.example.com/1.html',
        'http://www.example.com/2.html',
        'http://www.example.com/3.html',
    ]

    def parse(self, response):
        self.logger.info('A response from %s just arrived!', response.url)

Return multiple Requests and items from a single callback:

import scrapy

class MySpider(scrapy.Spider):
    name = 'example.com'
    allowed_domains = ['example.com']
    start_urls = [
        'http://www.example.com/1.html',
        'http://www.example.com/2.html',
        'http://www.example.com/3.html',
    ]

    def parse(self, response):
        for h3 in response.xpath('//h3').extract():
            yield {"title": h3}

        for url in response.xpath('//a/@href').extract():
            yield scrapy.Request(url, callback=self.parse)

Instead of start_urls you can use start_requests() directly; to give data more structure you can use Items:

import scrapy
from myproject.items import MyItem

class MySpider(scrapy.Spider):
    name = 'example.com'
    allowed_domains = ['example.com']

    def start_requests(self):
        yield scrapy.Request('http://www.example.com/1.html', self.parse)
        yield scrapy.Request('http://www.example.com/2.html', self.parse)
        yield scrapy.Request('http://www.example.com/3.html', self.parse)

    def parse(self, response):
        for h3 in response.xpath('//h3').extract():
            yield MyItem(title=h3)

        for url in response.xpath('//a/@href').extract():
            yield scrapy.Request(url, callback=self.parse)

Spider arguments

Spiders can receive arguments that modify their behaviour. Some common uses for spider arguments are to define the start URLs or to restrict the crawl to certain sections of the site, but they can be used to configure any functionality of the spider.

Spider arguments are passed through the crawl command using the -a option. For example:

scrapy crawl myspider -a category=electronics

Spiders receive arguments in their constructors:

import scrapy

class MySpider(scrapy.Spider):
    name = 'myspider'

    def __init__(self, category=None, *args, **kwargs):
        super(MySpider, self).__init__(*args, **kwargs)
        self.start_urls = ['http://www.example.com/categories/%s' % category]
        # ...

Spider arguments can also be passed through the Scrapyd schedule.json API. See Scrapyd documentation.

Generic Spiders

Scrapy comes with some useful generic spiders that you can use to subclass your spiders from. Their aim is to provide convenient functionality for a few common scraping cases, like following all links on a site based on certain rules, crawling from Sitemaps, or parsing an XML/CSV feed.

For the examples used in the following spiders, we’ll assume you have a project with a TestItem declared in a myproject.items module:

import scrapy

class TestItem(scrapy.Item):
    id = scrapy.Field()
    name = scrapy.Field()
    description = scrapy.Field()
CrawlSpider
class scrapy.spiders.CrawlSpider

This is the most commonly used spider for crawling regular websites, as it provides a convenient mechanism for following links by defining a set of rules. It may not be the best suited for your particular web sites or project, but it’s generic enough for several cases, so you can start from it and override it as needed for more custom functionality, or just implement your own spider.

Apart from the attributes inherited from Spider (that you must specify), this class supports a new attribute:

rules

Which is a list of one (or more) Rule objects. Each Rule defines a certain behaviour for crawling the site. Rules objects are described below. If multiple rules match the same link, the first one will be used, according to the order they’re defined in this attribute.

This spider also exposes an overrideable method:

parse_start_url(response)

This method is called for the start_urls responses. It allows to parse the initial responses and must return either an Item object, a Request object, or an iterable containing any of them.

Crawling rules
class scrapy.spiders.Rule(link_extractor, callback=None, cb_kwargs=None, follow=None, process_links=None, process_request=None)

link_extractor is a Link Extractor object which defines how links will be extracted from each crawled page.

callback is a callable or a string (in which case a method from the spider object with that name will be used) to be called for each link extracted with the specified link_extractor. This callback receives a response as its first argument and must return a list containing Item and/or Request objects (or any subclass of them).

Warning

When writing crawl spider rules, avoid using parse as callback, since the CrawlSpider uses the parse method itself to implement its logic. So if you override the parse method, the crawl spider will no longer work.

cb_kwargs is a dict containing the keyword arguments to be passed to the callback function.

follow is a boolean which specifies if links should be followed from each response extracted with this rule. If callback is None follow defaults to True, otherwise it defaults to False.

process_links is a callable, or a string (in which case a method from the spider object with that name will be used) which will be called for each list of links extracted from each response using the specified link_extractor. This is mainly used for filtering purposes.

process_request is a callable, or a string (in which case a method from the spider object with that name will be used) which will be called with every request extracted by this rule, and must return a request or None (to filter out the request).

CrawlSpider example

Let’s now take a look at an example CrawlSpider with rules:

import scrapy
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor

class MySpider(CrawlSpider):
    name = 'example.com'
    allowed_domains = ['example.com']
    start_urls = ['http://www.example.com']

    rules = (
        # Extract links matching 'category.php' (but not matching 'subsection.php')
        # and follow links from them (since no callback means follow=True by default).
        Rule(LinkExtractor(allow=('category\.php', ), deny=('subsection\.php', ))),

        # Extract links matching 'item.php' and parse them with the spider's method parse_item
        Rule(LinkExtractor(allow=('item\.php', )), callback='parse_item'),
    )

    def parse_item(self, response):
        self.logger.info('Hi, this is an item page! %s', response.url)
        item = scrapy.Item()
        item['id'] = response.xpath('//td[@id="item_id"]/text()').re(r'ID: (\d+)')
        item['name'] = response.xpath('//td[@id="item_name"]/text()').extract()
        item['description'] = response.xpath('//td[@id="item_description"]/text()').extract()
        return item

This spider would start crawling example.com’s home page, collecting category links, and item links, parsing the latter with the parse_item method. For each item response, some data will be extracted from the HTML using XPath, and an Item will be filled with it.

XMLFeedSpider
class scrapy.spiders.XMLFeedSpider

XMLFeedSpider is designed for parsing XML feeds by iterating through them by a certain node name. The iterator can be chosen from: iternodes, xml, and html. It’s recommended to use the iternodes iterator for performance reasons, since the xml and html iterators generate the whole DOM at once in order to parse it. However, using html as the iterator may be useful when parsing XML with bad markup.

To set the iterator and the tag name, you must define the following class attributes:

iterator

A string which defines the iterator to use. It can be either:

  • 'iternodes' - a fast iterator based on regular expressions
  • 'html' - an iterator which uses Selector. Keep in mind this uses DOM parsing and must load all DOM in memory which could be a problem for big feeds
  • 'xml' - an iterator which uses Selector. Keep in mind this uses DOM parsing and must load all DOM in memory which could be a problem for big feeds

It defaults to: 'iternodes'.

itertag

A string with the name of the node (or element) to iterate in. Example:

itertag = 'product'
namespaces

A list of (prefix, uri) tuples which define the namespaces available in that document that will be processed with this spider. The prefix and uri will be used to automatically register namespaces using the register_namespace() method.

You can then specify nodes with namespaces in the itertag attribute.

Example:

class YourSpider(XMLFeedSpider):

    namespaces = [('n', 'http://www.sitemaps.org/schemas/sitemap/0.9')]
    itertag = 'n:url'
    # ...

Apart from these new attributes, this spider has the following overrideable methods too:

adapt_response(response)

A method that receives the response as soon as it arrives from the spider middleware, before the spider starts parsing it. It can be used to modify the response body before parsing it. This method receives a response and also returns a response (it could be the same or another one).

parse_node(response, selector)

This method is called for the nodes matching the provided tag name (itertag). Receives the response and an Selector for each node. Overriding this method is mandatory. Otherwise, you spider won’t work. This method must return either a Item object, a Request object, or an iterable containing any of them.

process_results(response, results)

This method is called for each result (item or request) returned by the spider, and it’s intended to perform any last time processing required before returning the results to the framework core, for example setting the item IDs. It receives a list of results and the response which originated those results. It must return a list of results (Items or Requests).

XMLFeedSpider example

These spiders are pretty easy to use, let’s have a look at one example:

from scrapy.spiders import XMLFeedSpider
from myproject.items import TestItem

class MySpider(XMLFeedSpider):
    name = 'example.com'
    allowed_domains = ['example.com']
    start_urls = ['http://www.example.com/feed.xml']
    iterator = 'iternodes'  # This is actually unnecessary, since it's the default value
    itertag = 'item'

    def parse_node(self, response, node):
        self.logger.info('Hi, this is a <%s> node!: %s', self.itertag, ''.join(node.extract()))

        item = TestItem()
        item['id'] = node.xpath('@id').extract()
        item['name'] = node.xpath('name').extract()
        item['description'] = node.xpath('description').extract()
        return item

Basically what we did up there was to create a spider that downloads a feed from the given start_urls, and then iterates through each of its item tags, prints them out, and stores some random data in an Item.

CSVFeedSpider
class scrapy.spiders.CSVFeedSpider

This spider is very similar to the XMLFeedSpider, except that it iterates over rows, instead of nodes. The method that gets called in each iteration is parse_row().

delimiter

A string with the separator character for each field in the CSV file Defaults to ',' (comma).

quotechar

A string with the enclosure character for each field in the CSV file Defaults to '"' (quotation mark).

headers

A list of the rows contained in the file CSV feed which will be used to extract fields from it.

parse_row(response, row)

Receives a response and a dict (representing each row) with a key for each provided (or detected) header of the CSV file. This spider also gives the opportunity to override adapt_response and process_results methods for pre- and post-processing purposes.

CSVFeedSpider example

Let’s see an example similar to the previous one, but using a CSVFeedSpider:

from scrapy.spiders import CSVFeedSpider
from myproject.items import TestItem

class MySpider(CSVFeedSpider):
    name = 'example.com'
    allowed_domains = ['example.com']
    start_urls = ['http://www.example.com/feed.csv']
    delimiter = ';'
    quotechar = "'"
    headers = ['id', 'name', 'description']

    def parse_row(self, response, row):
        self.logger.info('Hi, this is a row!: %r', row)

        item = TestItem()
        item['id'] = row['id']
        item['name'] = row['name']
        item['description'] = row['description']
        return item
SitemapSpider
class scrapy.spiders.SitemapSpider

SitemapSpider allows you to crawl a site by discovering the URLs using Sitemaps.

It supports nested sitemaps and discovering sitemap urls from robots.txt.

sitemap_urls

A list of urls pointing to the sitemaps whose urls you want to crawl.

You can also point to a robots.txt and it will be parsed to extract sitemap urls from it.

sitemap_rules

A list of tuples (regex, callback) where:

  • regex is a regular expression to match urls extracted from sitemaps. regex can be either a str or a compiled regex object.
  • callback is the callback to use for processing the urls that match the regular expression. callback can be a string (indicating the name of a spider method) or a callable.

For example:

sitemap_rules = [('/product/', 'parse_product')]

Rules are applied in order, and only the first one that matches will be used.

If you omit this attribute, all urls found in sitemaps will be processed with the parse callback.

sitemap_follow

A list of regexes of sitemap that should be followed. This is is only for sites that use Sitemap index files that point to other sitemap files.

By default, all sitemaps are followed.

Specifies if alternate links for one url should be followed. These are links for the same website in another language passed within the same url block.

For example:

<url>
    <loc>http://example.com/</loc>
    <xhtml:link rel="alternate" hreflang="de" href="http://example.com/de"/>
</url>

With sitemap_alternate_links set, this would retrieve both URLs. With sitemap_alternate_links disabled, only http://example.com/ would be retrieved.

Default is sitemap_alternate_links disabled.

SitemapSpider examples

Simplest example: process all urls discovered through sitemaps using the parse callback:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['http://www.example.com/sitemap.xml']

    def parse(self, response):
        pass # ... scrape item here ...

Process some urls with certain callback and other urls with a different callback:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['http://www.example.com/sitemap.xml']
    sitemap_rules = [
        ('/product/', 'parse_product'),
        ('/category/', 'parse_category'),
    ]

    def parse_product(self, response):
        pass # ... scrape product ...

    def parse_category(self, response):
        pass # ... scrape category ...

Follow sitemaps defined in the robots.txt file and only follow sitemaps whose url contains /sitemap_shop:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['http://www.example.com/robots.txt']
    sitemap_rules = [
        ('/shop/', 'parse_shop'),
    ]
    sitemap_follow = ['/sitemap_shops']

    def parse_shop(self, response):
        pass # ... scrape shop here ...

Combine SitemapSpider with other sources of urls:

from scrapy.spiders import SitemapSpider

class MySpider(SitemapSpider):
    sitemap_urls = ['http://www.example.com/robots.txt']
    sitemap_rules = [
        ('/shop/', 'parse_shop'),
    ]

    other_urls = ['http://www.example.com/about']

    def start_requests(self):
        requests = list(super(MySpider, self).start_requests())
        requests += [scrapy.Request(x, self.parse_other) for x in self.other_urls]
        return requests

    def parse_shop(self, response):
        pass # ... scrape shop here ...

    def parse_other(self, response):
        pass # ... scrape other here ...

Selectors

When you’re scraping web pages, the most common task you need to perform is to extract data from the HTML source. There are several libraries available to achieve this:

  • BeautifulSoup is a very popular web scraping library among Python programmers which constructs a Python object based on the structure of the HTML code and also deals with bad markup reasonably well, but it has one drawback: it’s slow.
  • lxml is an XML parsing library (which also parses HTML) with a pythonic API based on ElementTree. (lxml is not part of the Python standard library.)

Scrapy comes with its own mechanism for extracting data. They’re called selectors because they “select” certain parts of the HTML document specified either by XPath or CSS expressions.

XPath is a language for selecting nodes in XML documents, which can also be used with HTML. CSS is a language for applying styles to HTML documents. It defines selectors to associate those styles with specific HTML elements.

Scrapy selectors are built over the lxml library, which means they’re very similar in speed and parsing accuracy.

This page explains how selectors work and describes their API which is very small and simple, unlike the lxml API which is much bigger because the lxml library can be used for many other tasks, besides selecting markup documents.

For a complete reference of the selectors API see Selector reference

Using selectors

Constructing selectors

Scrapy selectors are instances of Selector class constructed by passing text or TextResponse object. It automatically chooses the best parsing rules (XML vs HTML) based on input type:

>>> from scrapy.selector import Selector
>>> from scrapy.http import HtmlResponse

Constructing from text:

>>> body = '<html><body><span>good</span></body></html>'
>>> Selector(text=body).xpath('//span/text()').extract()
[u'good']

Constructing from response:

>>> response = HtmlResponse(url='http://example.com', body=body)
>>> Selector(response=response).xpath('//span/text()').extract()
[u'good']

For convenience, response objects expose a selector on .selector attribute, it’s totally OK to use this shortcut when possible:

>>> response.selector.xpath('//span/text()').extract()
[u'good']
Using selectors

To explain how to use the selectors we’ll use the Scrapy shell (which provides interactive testing) and an example page located in the Scrapy documentation server:

Here’s its HTML code:

<html>
 <head>
  <base href='http://example.com/' />
  <title>Example website</title>
 </head>
 <body>
  <div id='images'>
   <a href='image1.html'>Name: My image 1 <br /><img src='image1_thumb.jpg' /></a>
   <a href='image2.html'>Name: My image 2 <br /><img src='image2_thumb.jpg' /></a>
   <a href='image3.html'>Name: My image 3 <br /><img src='image3_thumb.jpg' /></a>
   <a href='image4.html'>Name: My image 4 <br /><img src='image4_thumb.jpg' /></a>
   <a href='image5.html'>Name: My image 5 <br /><img src='image5_thumb.jpg' /></a>
  </div>
 </body>
</html>

First, let’s open the shell:

scrapy shell http://doc.scrapy.org/en/latest/_static/selectors-sample1.html

Then, after the shell loads, you’ll have the response available as response shell variable, and its attached selector in response.selector attribute.

Since we’re dealing with HTML, the selector will automatically use an HTML parser.

So, by looking at the HTML code of that page, let’s construct an XPath for selecting the text inside the title tag:

>>> response.selector.xpath('//title/text()')
[<Selector (text) xpath=//title/text()>]

Querying responses using XPath and CSS is so common that responses include two convenience shortcuts: response.xpath() and response.css():

>>> response.xpath('//title/text()')
[<Selector (text) xpath=//title/text()>]
>>> response.css('title::text')
[<Selector (text) xpath=//title/text()>]

As you can see, .xpath() and .css() methods return a SelectorList instance, which is a list of new selectors. This API can be used for quickly selecting nested data:

>>> response.css('img').xpath('@src').extract()
[u'image1_thumb.jpg',
 u'image2_thumb.jpg',
 u'image3_thumb.jpg',
 u'image4_thumb.jpg',
 u'image5_thumb.jpg']

To actually extract the textual data, you must call the selector .extract() method, as follows:

>>> response.xpath('//title/text()').extract()
[u'Example website']

If you want to extract only first matched element, you can call the selector .extract_first()

>>> response.xpath('//div[@id="images"]/a/text()').extract_first()
u'Name: My image 1 '

It returns None if no element was found:

>>> response.xpath('//div[@id="not-exists"]/text()').extract_first() is None
True

A default return value can be provided as an argument, to be used instead of None:

>>> response.xpath('//div[@id="not-exists"]/text()').extract_first(default='not-found')
'not-found'

Notice that CSS selectors can select text or attribute nodes using CSS3 pseudo-elements:

>>> response.css('title::text').extract()
[u'Example website']

Now we’re going to get the base URL and some image links:

>>> response.xpath('//base/@href').extract()
[u'http://example.com/']

>>> response.css('base::attr(href)').extract()
[u'http://example.com/']

>>> response.xpath('//a[contains(@href, "image")]/@href').extract()
[u'image1.html',
 u'image2.html',
 u'image3.html',
 u'image4.html',
 u'image5.html']

>>> response.css('a[href*=image]::attr(href)').extract()
[u'image1.html',
 u'image2.html',
 u'image3.html',
 u'image4.html',
 u'image5.html']

>>> response.xpath('//a[contains(@href, "image")]/img/@src').extract()
[u'image1_thumb.jpg',
 u'image2_thumb.jpg',
 u'image3_thumb.jpg',
 u'image4_thumb.jpg',
 u'image5_thumb.jpg']

>>> response.css('a[href*=image] img::attr(src)').extract()
[u'image1_thumb.jpg',
 u'image2_thumb.jpg',
 u'image3_thumb.jpg',
 u'image4_thumb.jpg',
 u'image5_thumb.jpg']
Nesting selectors

The selection methods (.xpath() or .css()) return a list of selectors of the same type, so you can call the selection methods for those selectors too. Here’s an example:

>>> links = response.xpath('//a[contains(@href, "image")]')
>>> links.extract()
[u'<a href="image1.html">Name: My image 1 <br><img src="image1_thumb.jpg"></a>',
 u'<a href="image2.html">Name: My image 2 <br><img src="image2_thumb.jpg"></a>',
 u'<a href="image3.html">Name: My image 3 <br><img src="image3_thumb.jpg"></a>',
 u'<a href="image4.html">Name: My image 4 <br><img src="image4_thumb.jpg"></a>',
 u'<a href="image5.html">Name: My image 5 <br><img src="image5_thumb.jpg"></a>']

>>> for index, link in enumerate(links):
...     args = (index, link.xpath('@href').extract(), link.xpath('img/@src').extract())
...     print 'Link number %d points to url %s and image %s' % args

Link number 0 points to url [u'image1.html'] and image [u'image1_thumb.jpg']
Link number 1 points to url [u'image2.html'] and image [u'image2_thumb.jpg']
Link number 2 points to url [u'image3.html'] and image [u'image3_thumb.jpg']
Link number 3 points to url [u'image4.html'] and image [u'image4_thumb.jpg']
Link number 4 points to url [u'image5.html'] and image [u'image5_thumb.jpg']
Using selectors with regular expressions

Selector also has a .re() method for extracting data using regular expressions. However, unlike using .xpath() or .css() methods, .re() returns a list of unicode strings. So you can’t construct nested .re() calls.

Here’s an example used to extract image names from the HTML code above:

>>> response.xpath('//a[contains(@href, "image")]/text()').re(r'Name:\s*(.*)')
[u'My image 1',
 u'My image 2',
 u'My image 3',
 u'My image 4',
 u'My image 5']

There’s an additional helper reciprocating .extract_first() for .re(), named .re_first(). Use it to extract just the first matching string:

>>> response.xpath('//a[contains(@href, "image")]/text()').re_first(r'Name:\s*(.*)')
u'My image 1'
Working with relative XPaths

Keep in mind that if you are nesting selectors and use an XPath that starts with /, that XPath will be absolute to the document and not relative to the Selector you’re calling it from.

For example, suppose you want to extract all <p> elements inside <div> elements. First, you would get all <div> elements:

>>> divs = response.xpath('//div')

At first, you may be tempted to use the following approach, which is wrong, as it actually extracts all <p> elements from the document, not only those inside <div> elements:

>>> for p in divs.xpath('//p'):  # this is wrong - gets all <p> from the whole document
...     print p.extract()

This is the proper way to do it (note the dot prefixing the .//p XPath):

>>> for p in divs.xpath('.//p'):  # extracts all <p> inside
...     print p.extract()

Another common case would be to extract all direct <p> children:

>>> for p in divs.xpath('p'):
...     print p.extract()

For more details about relative XPaths see the Location Paths section in the XPath specification.

Using EXSLT extensions

Being built atop lxml, Scrapy selectors also support some EXSLT extensions and come with these pre-registered namespaces to use in XPath expressions:

prefix namespace usage
re http://exslt.org/regular-expressions regular expressions
set http://exslt.org/sets set manipulation
Regular expressions

The test() function, for example, can prove quite useful when XPath’s starts-with() or contains() are not sufficient.

Example selecting links in list item with a “class” attribute ending with a digit:

>>> from scrapy import Selector
>>> doc = """
... <div>
...     <ul>
...         <li class="item-0"><a href="link1.html">first item</a></li>
...         <li class="item-1"><a href="link2.html">second item</a></li>
...         <li class="item-inactive"><a href="link3.html">third item</a></li>
...         <li class="item-1"><a href="link4.html">fourth item</a></li>
...         <li class="item-0"><a href="link5.html">fifth item</a></li>
...     </ul>
... </div>
... """
>>> sel = Selector(text=doc, type="html")
>>> sel.xpath('//li//@href').extract()
[u'link1.html', u'link2.html', u'link3.html', u'link4.html', u'link5.html']
>>> sel.xpath('//li[re:test(@class, "item-\d$")]//@href').extract()
[u'link1.html', u'link2.html', u'link4.html', u'link5.html']
>>>

Warning

C library libxslt doesn’t natively support EXSLT regular expressions so lxml‘s implementation uses hooks to Python’s re module. Thus, using regexp functions in your XPath expressions may add a small performance penalty.

Set operations

These can be handy for excluding parts of a document tree before extracting text elements for example.

Example extracting microdata (sample content taken from http://schema.org/Product) with groups of itemscopes and corresponding itemprops:

>>> doc = """
... <div itemscope itemtype="http://schema.org/Product">
...   <span itemprop="name">Kenmore White 17" Microwave</span>
...   <img src="kenmore-microwave-17in.jpg" alt='Kenmore 17" Microwave' />
...   <div itemprop="aggregateRating"
...     itemscope itemtype="http://schema.org/AggregateRating">
...    Rated <span itemprop="ratingValue">3.5</span>/5
...    based on <span itemprop="reviewCount">11</span> customer reviews
...   </div>
...
...   <div itemprop="offers" itemscope itemtype="http://schema.org/Offer">
...     <span itemprop="price">$55.00</span>
...     <link itemprop="availability" href="http://schema.org/InStock" />In stock
...   </div>
...
...   Product description:
...   <span itemprop="description">0.7 cubic feet countertop microwave.
...   Has six preset cooking categories and convenience features like
...   Add-A-Minute and Child Lock.</span>
...
...   Customer reviews:
...
...   <div itemprop="review" itemscope itemtype="http://schema.org/Review">
...     <span itemprop="name">Not a happy camper</span> -
...     by <span itemprop="author">Ellie</span>,
...     <meta itemprop="datePublished" content="2011-04-01">April 1, 2011
...     <div itemprop="reviewRating" itemscope itemtype="http://schema.org/Rating">
...       <meta itemprop="worstRating" content = "1">
...       <span itemprop="ratingValue">1</span>/
...       <span itemprop="bestRating">5</span>stars
...     </div>
...     <span itemprop="description">The lamp burned out and now I have to replace
...     it. </span>
...   </div>
...
...   <div itemprop="review" itemscope itemtype="http://schema.org/Review">
...     <span itemprop="name">Value purchase</span> -
...     by <span itemprop="author">Lucas</span>,
...     <meta itemprop="datePublished" content="2011-03-25">March 25, 2011
...     <div itemprop="reviewRating" itemscope itemtype="http://schema.org/Rating">
...       <meta itemprop="worstRating" content = "1"/>
...       <span itemprop="ratingValue">4</span>/
...       <span itemprop="bestRating">5</span>stars
...     </div>
...     <span itemprop="description">Great microwave for the price. It is small and
...     fits in my apartment.</span>
...   </div>
...   ...
... </div>
... """
>>> sel = Selector(text=doc, type="html")
>>> for scope in sel.xpath('//div[@itemscope]'):
...     print "current scope:", scope.xpath('@itemtype').extract()
...     props = scope.xpath('''
...                 set:difference(./descendant::*/@itemprop,
...                                .//*[@itemscope]/*/@itemprop)''')
...     print "    properties:", props.extract()
...     print

current scope: [u'http://schema.org/Product']
    properties: [u'name', u'aggregateRating', u'offers', u'description', u'review', u'review']

current scope: [u'http://schema.org/AggregateRating']
    properties: [u'ratingValue', u'reviewCount']

current scope: [u'http://schema.org/Offer']
    properties: [u'price', u'availability']

current scope: [u'http://schema.org/Review']
    properties: [u'name', u'author', u'datePublished', u'reviewRating', u'description']

current scope: [u'http://schema.org/Rating']
    properties: [u'worstRating', u'ratingValue', u'bestRating']

current scope: [u'http://schema.org/Review']
    properties: [u'name', u'author', u'datePublished', u'reviewRating', u'description']

current scope: [u'http://schema.org/Rating']
    properties: [u'worstRating', u'ratingValue', u'bestRating']

>>>

Here we first iterate over itemscope elements, and for each one, we look for all itemprops elements and exclude those that are themselves inside another itemscope.

Some XPath tips

Here are some tips that you may find useful when using XPath with Scrapy selectors, based on this post from ScrapingHub’s blog. If you are not much familiar with XPath yet, you may want to take a look first at this XPath tutorial.

Using text nodes in a condition

When you need to use the text content as argument to an XPath string function, avoid using .//text() and use just . instead.

This is because the expression .//text() yields a collection of text elements – a node-set. And when a node-set is converted to a string, which happens when it is passed as argument to a string function like contains() or starts-with(), it results in the text for the first element only.

Example:

>>> from scrapy import Selector
>>> sel = Selector(text='<a href="#">Click here to go to the <strong>Next Page</strong></a>')

Converting a node-set to string:

>>> sel.xpath('//a//text()').extract() # take a peek at the node-set
[u'Click here to go to the ', u'Next Page']
>>> sel.xpath("string(//a[1]//text())").extract() # convert it to string
[u'Click here to go to the ']

A node converted to a string, however, puts together the text of itself plus of all its descendants:

>>> sel.xpath("//a[1]").extract() # select the first node
[u'<a href="#">Click here to go to the <strong>Next Page</strong></a>']
>>> sel.xpath("string(//a[1])").extract() # convert it to string
[u'Click here to go to the Next Page']

So, using the .//text() node-set won’t select anything in this case:

>>> sel.xpath("//a[contains(.//text(), 'Next Page')]").extract()
[]

But using the . to mean the node, works:

>>> sel.xpath("//a[contains(., 'Next Page')]").extract()
[u'<a href="#">Click here to go to the <strong>Next Page</strong></a>']
Beware of the difference between //node[1] and (//node)[1]

//node[1] selects all the nodes occurring first under their respective parents.

(//node)[1] selects all the nodes in the document, and then gets only the first of them.

Example:

>>> from scrapy import Selector
>>> sel = Selector(text="""
....:     <ul class="list">
....:         <li>1</li>
....:         <li>2</li>
....:         <li>3</li>
....:     </ul>
....:     <ul class="list">
....:         <li>4</li>
....:         <li>5</li>
....:         <li>6</li>
....:     </ul>""")
>>> xp = lambda x: sel.xpath(x).extract()

This gets all first <li> elements under whatever it is its parent:

>>> xp("//li[1]")
[u'<li>1</li>', u'<li>4</li>']

And this gets the first <li> element in the whole document:

>>> xp("(//li)[1]")
[u'<li>1</li>']

This gets all first <li> elements under an <ul> parent:

>>> xp("//ul/li[1]")
[u'<li>1</li>', u'<li>4</li>']

And this gets the first <li> element under an <ul> parent in the whole document:

>>> xp("(//ul/li)[1]")
[u'<li>1</li>']
When querying by class, consider using CSS

Because an element can contain multiple CSS classes, the XPath way to select elements by class is the rather verbose:

*[contains(concat(' ', normalize-space(@class), ' '), ' someclass ')]

If you use @class='someclass' you may end up missing elements that have other classes, and if you just use contains(@class, 'someclass') to make up for that you may end up with more elements that you want, if they have a different class name that shares the string someclass.

As it turns out, Scrapy selectors allow you to chain selectors, so most of the time you can just select by class using CSS and then switch to XPath when needed:

>>> from scrapy import Selector
>>> sel = Selector(text='<div class="hero shout"><time datetime="2014-07-23 19:00">Special date</time></div>')
>>> sel.css('.shout').xpath('./time/@datetime').extract()
[u'2014-07-23 19:00']

This is cleaner than using the verbose XPath trick shown above. Just remember to use the . in the XPath expressions that will follow.

Built-in Selectors reference

class scrapy.selector.Selector(response=None, text=None, type=None)

An instance of Selector is a wrapper over response to select certain parts of its content.

response is an HtmlResponse or an XmlResponse object that will be used for selecting and extracting data.

text is a unicode string or utf-8 encoded text for cases when a response isn’t available. Using text and response together is undefined behavior.

type defines the selector type, it can be "html", "xml" or None (default).

If type is None, the selector automatically chooses the best type based on response type (see below), or defaults to "html" in case it is used together with text.

If type is None and a response is passed, the selector type is inferred from the response type as follows:

Otherwise, if type is set, the selector type will be forced and no detection will occur.

xpath(query)

Find nodes matching the xpath query and return the result as a SelectorList instance with all elements flattened. List elements implement Selector interface too.

query is a string containing the XPATH query to apply.

Note

For convenience, this method can be called as response.xpath()

css(query)

Apply the given CSS selector and return a SelectorList instance.

query is a string containing the CSS selector to apply.

In the background, CSS queries are translated into XPath queries using cssselect library and run .xpath() method.

Note

For convenience this method can be called as response.css()

extract()

Serialize and return the matched nodes as a list of unicode strings. Percent encoded content is unquoted.

re(regex)

Apply the given regex and return a list of unicode strings with the matches.

regex can be either a compiled regular expression or a string which will be compiled to a regular expression using re.compile(regex)

register_namespace(prefix, uri)

Register the given namespace to be used in this Selector. Without registering namespaces you can’t select or extract data from non-standard namespaces. See examples below.

remove_namespaces()

Remove all namespaces, allowing to traverse the document using namespace-less xpaths. See example below.

__nonzero__()

Returns True if there is any real content selected or False otherwise. In other words, the boolean value of a Selector is given by the contents it selects.

SelectorList objects
class scrapy.selector.SelectorList

The SelectorList class is a subclass of the builtin list class, which provides a few additional methods.

xpath(query)

Call the .xpath() method for each element in this list and return their results flattened as another SelectorList.

query is the same argument as the one in Selector.xpath()

css(query)

Call the .css() method for each element in this list and return their results flattened as another SelectorList.

query is the same argument as the one in Selector.css()

extract()

Call the .extract() method for each element in this list and return their results flattened, as a list of unicode strings.

re()

Call the .re() method for each element in this list and return their results flattened, as a list of unicode strings.

__nonzero__()

returns True if the list is not empty, False otherwise.

Selector examples on HTML response

Here’s a couple of Selector examples to illustrate several concepts. In all cases, we assume there is already a Selector instantiated with a HtmlResponse object like this:

sel = Selector(html_response)

  1. Select all <h1> elements from an HTML response body, returning a list of Selector objects (ie. a SelectorList object):

    sel.xpath("//h1")

  2. Extract the text of all <h1> elements from an HTML response body, returning a list of unicode strings:

    sel.xpath("//h1").extract()         # this includes the h1 tag
sel.xpath("//h1/text()").extract()  # this excludes the h1 tag

  3. Iterate over all <p> tags and print their class attribute:

    for node in sel.xpath("//p"):
    print node.xpath("@class").extract()
Selector examples on XML response

Here’s a couple of examples to illustrate several concepts. In both cases we assume there is already a Selector instantiated with an XmlResponse object like this:

sel = Selector(xml_response)

  1. Select all <product> elements from an XML response body, returning a list of Selector objects (ie. a SelectorList object):

    sel.xpath("//product")

  2. Extract all prices from a Google Base XML feed which requires registering a namespace:

    sel.register_namespace("g", "http://base.google.com/ns/1.0")
sel.xpath("//g:price").extract()
Removing namespaces

When dealing with scraping projects, it is often quite convenient to get rid of namespaces altogether and just work with element names, to write more simple/convenient XPaths. You can use the Selector.remove_namespaces() method for that.

Let’s show an example that illustrates this with Github blog atom feed.

First, we open the shell with the url we want to scrape:

$ scrapy shell https://github.com/blog.atom

Once in the shell we can try selecting all <link> objects and see that it doesn’t work (because the Atom XML namespace is obfuscating those nodes):

>>> response.xpath("//link")
[]

But once we call the Selector.remove_namespaces() method, all nodes can be accessed directly by their names:

>>> response.selector.remove_namespaces()
>>> response.xpath("//link")
[<Selector xpath='//link' data=u'<link xmlns="http://www.w3.org/2005/Atom'>,
 <Selector xpath='//link' data=u'<link xmlns="http://www.w3.org/2005/Atom'>,
 ...

If you wonder why the namespace removal procedure isn’t always called by default instead of having to call it manually, this is because of two reasons, which, in order of relevance, are:

  1. Removing namespaces requires to iterate and modify all nodes in the document, which is a reasonably expensive operation to perform for all documents crawled by Scrapy
  2. There could be some cases where using namespaces is actually required, in case some element names clash between namespaces. These cases are very rare though.

Items

The main goal in scraping is to extract structured data from unstructured sources, typically, web pages. Scrapy spiders can return the extracted data as Python dicts. While convenient and familiar, Python dicts lack structure: it is easy to make a typo in a field name or return inconsistent data, especially in a larger project with many spiders.

To define common output data format Scrapy provides the Item class. Item objects are simple containers used to collect the scraped data. They provide a dictionary-like API with a convenient syntax for declaring their available fields.

Various Scrapy components use extra information provided by Items: exporters look at declared fields to figure out columns to export, serialization can be customized using Item fields metadata, trackref tracks Item instances to help finding memory leaks (see Debugging memory leaks with trackref), etc.

Declaring Items

Items are declared using a simple class definition syntax and Field objects. Here is an example:

import scrapy

class Product(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()
    stock = scrapy.Field()
    last_updated = scrapy.Field(serializer=str)

Note

Those familiar with Django will notice that Scrapy Items are declared similar to Django Models, except that Scrapy Items are much simpler as there is no concept of different field types.

Item Fields

Field objects are used to specify metadata for each field. For example, the serializer function for the last_updated field illustrated in the example above.

You can specify any kind of metadata for each field. There is no restriction on the values accepted by Field objects. For this same reason, there is no reference list of all available metadata keys. Each key defined in Field objects could be used by a different component, and only those components know about it. You can also define and use any other Field key in your project too, for your own needs. The main goal of Field objects is to provide a way to define all field metadata in one place. Typically, those components whose behaviour depends on each field use certain field keys to configure that behaviour. You must refer to their documentation to see which metadata keys are used by each component.

It’s important to note that the Field objects used to declare the item do not stay assigned as class attributes. Instead, they can be accessed through the Item.fields attribute.

Working with Items

Here are some examples of common tasks performed with items, using the Product item declared above. You will notice the API is very similar to the dict API.

Creating items
>>> product = Product(name='Desktop PC', price=1000)
>>> print product
Product(name='Desktop PC', price=1000)
Getting field values
>>> product['name']
Desktop PC
>>> product.get('name')
Desktop PC

>>> product['price']
1000

>>> product['last_updated']
Traceback (most recent call last):
    ...
KeyError: 'last_updated'

>>> product.get('last_updated', 'not set')
not set

>>> product['lala'] # getting unknown field
Traceback (most recent call last):
    ...
KeyError: 'lala'

>>> product.get('lala', 'unknown field')
'unknown field'

>>> 'name' in product  # is name field populated?
True

>>> 'last_updated' in product  # is last_updated populated?
False

>>> 'last_updated' in product.fields  # is last_updated a declared field?
True

>>> 'lala' in product.fields  # is lala a declared field?
False
Setting field values
>>> product['last_updated'] = 'today'
>>> product['last_updated']
today

>>> product['lala'] = 'test' # setting unknown field
Traceback (most recent call last):
    ...
KeyError: 'Product does not support field: lala'
Accessing all populated values

To access all populated values, just use the typical dict API:

>>> product.keys()
['price', 'name']

>>> product.items()
[('price', 1000), ('name', 'Desktop PC')]
Other common tasks

Copying items:

>>> product2 = Product(product)
>>> print product2
Product(name='Desktop PC', price=1000)

>>> product3 = product2.copy()
>>> print product3
Product(name='Desktop PC', price=1000)

Creating dicts from items:

>>> dict(product) # create a dict from all populated values
{'price': 1000, 'name': 'Desktop PC'}

Creating items from dicts:

>>> Product({'name': 'Laptop PC', 'price': 1500})
Product(price=1500, name='Laptop PC')

>>> Product({'name': 'Laptop PC', 'lala': 1500}) # warning: unknown field in dict
Traceback (most recent call last):
    ...
KeyError: 'Product does not support field: lala'

Extending Items

You can extend Items (to add more fields or to change some metadata for some fields) by declaring a subclass of your original Item.

For example:

class DiscountedProduct(Product):
    discount_percent = scrapy.Field(serializer=str)
    discount_expiration_date = scrapy.Field()

You can also extend field metadata by using the previous field metadata and appending more values, or changing existing values, like this:

class SpecificProduct(Product):
    name = scrapy.Field(Product.fields['name'], serializer=my_serializer)

That adds (or replaces) the serializer metadata key for the name field, keeping all the previously existing metadata values.

Item objects

class scrapy.item.Item([arg])

Return a new Item optionally initialized from the given argument.

Items replicate the standard dict API, including its constructor. The only additional attribute provided by Items is:

fields

A dictionary containing all declared fields for this Item, not only those populated. The keys are the field names and the values are the Field objects used in the Item declaration.

Field objects

class scrapy.item.Field([arg])

The Field class is just an alias to the built-in dict class and doesn’t provide any extra functionality or attributes. In other words, Field objects are plain-old Python dicts. A separate class is used to support the item declaration syntax based on class attributes.

Item Loaders

Item Loaders provide a convenient mechanism for populating scraped Items. Even though Items can be populated using their own dictionary-like API, Item Loaders provide a much more convenient API for populating them from a scraping process, by automating some common tasks like parsing the raw extracted data before assigning it.

In other words, Items provide the container of scraped data, while Item Loaders provide the mechanism for populating that container.

Item Loaders are designed to provide a flexible, efficient and easy mechanism for extending and overriding different field parsing rules, either by spider, or by source format (HTML, XML, etc) without becoming a nightmare to maintain.

Using Item Loaders to populate items

To use an Item Loader, you must first instantiate it. You can either instantiate it with a dict-like object (e.g. Item or dict) or without one, in which case an Item is automatically instantiated in the Item Loader constructor using the Item class specified in the ItemLoader.default_item_class attribute.

Then, you start collecting values into the Item Loader, typically using Selectors. You can add more than one value to the same item field; the Item Loader will know how to “join” those values later using a proper processing function.

Here is a typical Item Loader usage in a Spider, using the Product item declared in the Items chapter:

from scrapy.loader import ItemLoader
from myproject.items import Product

def parse(self, response):
    l = ItemLoader(item=Product(), response=response)
    l.add_xpath('name', '//div[@class="product_name"]')
    l.add_xpath('name', '//div[@class="product_title"]')
    l.add_xpath('price', '//p[@id="price"]')
    l.add_css('stock', 'p#stock]')
    l.add_value('last_updated', 'today') # you can also use literal values
    return l.load_item()

By quickly looking at that code, we can see the name field is being extracted from two different XPath locations in the page:

  1. //div[@class="product_name"]
  2. //div[@class="product_title"]

In other words, data is being collected by extracting it from two XPath locations, using the add_xpath() method. This is the data that will be assigned to the name field later.

Afterwards, similar calls are used for price and stock fields (the later using a CSS selector with the add_css() method), and finally the last_update field is populated directly with a literal value (today) using a different method: add_value().

Finally, when all data is collected, the ItemLoader.load_item() method is called which actually returns the item populated with the data previously extracted and collected with the add_xpath(), add_css(), and add_value() calls.

Input and Output processors

An Item Loader contains one input processor and one output processor for each (item) field. The input processor processes the extracted data as soon as it’s received (through the add_xpath(), add_css() or add_value() methods) and the result of the input processor is collected and kept inside the ItemLoader. After collecting all data, the ItemLoader.load_item() method is called to populate and get the populated Item object. That’s when the output processor is called with the data previously collected (and processed using the input processor). The result of the output processor is the final value that gets assigned to the item.

Let’s see an example to illustrate how the input and output processors are called for a particular field (the same applies for any other field):

l = ItemLoader(Product(), some_selector)
l.add_xpath('name', xpath1) # (1)
l.add_xpath('name', xpath2) # (2)
l.add_css('name', css) # (3)
l.add_value('name', 'test') # (4)
return l.load_item() # (5)

So what happens is:

  1. Data from xpath1 is extracted, and passed through the input processor of the name field. The result of the input processor is collected and kept in the Item Loader (but not yet assigned to the item).
  2. Data from xpath2 is extracted, and passed through the same input processor used in (1). The result of the input processor is appended to the data collected in (1) (if any).
  3. This case is similar to the previous ones, except that the data is extracted from the css CSS selector, and passed through the same input processor used in (1) and (2). The result of the input processor is appended to the data collected in (1) and (2) (if any).
  4. This case is also similar to the previous ones, except that the value to be collected is assigned directly, instead of being extracted from a XPath expression or a CSS selector. However, the value is still passed through the input processors. In this case, since the value is not iterable it is converted to an iterable of a single element before passing it to the input processor, because input processor always receive iterables.
  5. The data collected in steps (1), (2), (3) and (4) is passed through the output processor of the name field. The result of the output processor is the value assigned to the name field in the item.

It’s worth noticing that processors are just callable objects, which are called with the data to be parsed, and return a parsed value. So you can use any function as input or output processor. The only requirement is that they must accept one (and only one) positional argument, which will be an iterator.

Note

Both input and output processors must receive an iterator as their first argument. The output of those functions can be anything. The result of input processors will be appended to an internal list (in the Loader) containing the collected values (for that field). The result of the output processors is the value that will be finally assigned to the item.

The other thing you need to keep in mind is that the values returned by input processors are collected internally (in lists) and then passed to output processors to populate the fields.

Last, but not least, Scrapy comes with some commonly used processors built-in for convenience.

Declaring Item Loaders

Item Loaders are declared like Items, by using a class definition syntax. Here is an example:

from scrapy.loader import ItemLoader
from scrapy.loader.processors import TakeFirst, MapCompose, Join

class ProductLoader(ItemLoader):

    default_output_processor = TakeFirst()

    name_in = MapCompose(unicode.title)
    name_out = Join()

    price_in = MapCompose(unicode.strip)

    # ...

As you can see, input processors are declared using the _in suffix while output processors are declared using the _out suffix. And you can also declare a default input/output processors using the ItemLoader.default_input_processor and ItemLoader.default_output_processor attributes.

Declaring Input and Output Processors

As seen in the previous section, input and output processors can be declared in the Item Loader definition, and it’s very common to declare input processors this way. However, there is one more place where you can specify the input and output processors to use: in the Item Field metadata. Here is an example:

import scrapy
from scrapy.loader.processors import Join, MapCompose, TakeFirst
from w3lib.html import remove_tags

def filter_price(value):
    if value.isdigit():
        return value

class Product(scrapy.Item):
    name = scrapy.Field(
        input_processor=MapCompose(remove_tags),
        output_processor=Join(),
    )
    price = scrapy.Field(
        input_processor=MapCompose(remove_tags, filter_price),
        output_processor=TakeFirst(),
    )

>>> from scrapy.loader import ItemLoader
>>> il = ItemLoader(item=Product())
>>> il.add_value('name', [u'Welcome to my', u'<strong>website</strong>'])
>>> il.add_value('price', [u'&euro;', u'<span>1000</span>'])
>>> il.load_item()
{'name': u'Welcome to my website', 'price': u'1000'}

The precedence order, for both input and output processors, is as follows:

  1. Item Loader field-specific attributes: field_in and field_out (most precedence)
  2. Field metadata (input_processor and output_processor key)
  3. Item Loader defaults: ItemLoader.default_input_processor() and ItemLoader.default_output_processor() (least precedence)

See also: Reusing and extending Item Loaders.

Item Loader Context

The Item Loader Context is a dict of arbitrary key/values which is shared among all input and output processors in the Item Loader. It can be passed when declaring, instantiating or using Item Loader. They are used to modify the behaviour of the input/output processors.

For example, suppose you have a function parse_length which receives a text value and extracts a length from it:

def parse_length(text, loader_context):
    unit = loader_context.get('unit', 'm')
    # ... length parsing code goes here ...
    return parsed_length

By accepting a loader_context argument the function is explicitly telling the Item Loader that it’s able to receive an Item Loader context, so the Item Loader passes the currently active context when calling it, and the processor function (parse_length in this case) can thus use them.

There are several ways to modify Item Loader context values:

  1. By modifying the currently active Item Loader context (context attribute):

    loader = ItemLoader(product)
loader.context['unit'] = 'cm'

  2. On Item Loader instantiation (the keyword arguments of Item Loader constructor are stored in the Item Loader context):

    loader = ItemLoader(product, unit='cm')

  3. On Item Loader declaration, for those input/output processors that support instantiating them with an Item Loader context. MapCompose is one of them:

    class ProductLoader(ItemLoader):
    length_out = MapCompose(parse_length, unit='cm')

ItemLoader objects

class scrapy.loader.ItemLoader([item, selector, response, ]**kwargs)

Return a new Item Loader for populating the given Item. If no item is given, one is instantiated automatically using the class in default_item_class.

When instantiated with a selector or a response parameters the ItemLoader class provides convenient mechanisms for extracting data from web pages using selectors.

Parameters:

The item, selector, response and the remaining keyword arguments are assigned to the Loader context (accessible through the context attribute).

ItemLoader instances have the following methods:

get_value(value, *processors, **kwargs)

Process the given value by the given processors and keyword arguments.

Available keyword arguments:

Parameters:re (str or compiled regex) – a regular expression to use for extracting data from the given value using extract_regex() method, applied before processors

Examples:

>>> from scrapy.loader.processors import TakeFirst
>>> loader.get_value(u'name: foo', TakeFirst(), unicode.upper, re='name: (.+)')
'FOO`
add_value(field_name, value, *processors, **kwargs)

Process and then add the given value for the given field.

The value is first passed through get_value() by giving the processors and kwargs, and then passed through the field input processor and its result appended to the data collected for that field. If the field already contains collected data, the new data is added.

The given field_name can be None, in which case values for multiple fields may be added. And the processed value should be a dict with field_name mapped to values.

Examples:

loader.add_value('name', u'Color TV')
loader.add_value('colours', [u'white', u'blue'])
loader.add_value('length', u'100')
loader.add_value('name', u'name: foo', TakeFirst(), re='name: (.+)')
loader.add_value(None, {'name': u'foo', 'sex': u'male'})
replace_value(field_name, value, *processors, **kwargs)

Similar to add_value() but replaces the collected data with the new value instead of adding it.

get_xpath(xpath, *processors, **kwargs)

Similar to ItemLoader.get_value() but receives an XPath instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

Parameters:
  • xpath (str) – the XPath to extract data from
  • re (str or compiled regex) – a regular expression to use for extracting data from the selected XPath region

Examples:

# HTML snippet: <p class="product-name">Color TV</p>
loader.get_xpath('//p[@class="product-name"]')
# HTML snippet: <p id="price">the price is $1200</p>
loader.get_xpath('//p[@id="price"]', TakeFirst(), re='the price is (.*)')
add_xpath(field_name, xpath, *processors, **kwargs)

Similar to ItemLoader.add_value() but receives an XPath instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

See get_xpath() for kwargs.

Parameters:xpath (str) – the XPath to extract data from

Examples:

# HTML snippet: <p class="product-name">Color TV</p>
loader.add_xpath('name', '//p[@class="product-name"]')
# HTML snippet: <p id="price">the price is $1200</p>
loader.add_xpath('price', '//p[@id="price"]', re='the price is (.*)')
replace_xpath(field_name, xpath, *processors, **kwargs)

Similar to add_xpath() but replaces collected data instead of adding it.

get_css(css, *processors, **kwargs)

Similar to ItemLoader.get_value() but receives a CSS selector instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

Parameters:
  • css (str) – the CSS selector to extract data from
  • re (str or compiled regex) – a regular expression to use for extracting data from the selected CSS region

Examples:

# HTML snippet: <p class="product-name">Color TV</p>
loader.get_css('p.product-name')
# HTML snippet: <p id="price">the price is $1200</p>
loader.get_css('p#price', TakeFirst(), re='the price is (.*)')
add_css(field_name, css, *processors, **kwargs)

Similar to ItemLoader.add_value() but receives a CSS selector instead of a value, which is used to extract a list of unicode strings from the selector associated with this ItemLoader.

See get_css() for kwargs.

Parameters:css (str) – the CSS selector to extract data from

Examples:

# HTML snippet: <p class="product-name">Color TV</p>
loader.add_css('name', 'p.product-name')
# HTML snippet: <p id="price">the price is $1200</p>
loader.add_css('price', 'p#price', re='the price is (.*)')
replace_css(field_name, css, *processors, **kwargs)

Similar to add_css() but replaces collected data instead of adding it.

load_item()

Populate the item with the data collected so far, and return it. The data collected is first passed through the output processors to get the final value to assign to each item field.

get_collected_values(field_name)

Return the collected values for the given field.

get_output_value(field_name)

Return the collected values parsed using the output processor, for the given field. This method doesn’t populate or modify the item at all.

get_input_processor(field_name)

Return the input processor for the given field.

get_output_processor(field_name)

Return the output processor for the given field.

ItemLoader instances have the following attributes:

item

The Item object being parsed by this Item Loader.

context

The currently active Context of this Item Loader.

default_item_class

An Item class (or factory), used to instantiate items when not given in the constructor.

default_input_processor

The default input processor to use for those fields which don’t specify one.

default_output_processor

The default output processor to use for those fields which don’t specify one.

default_selector_class

The class used to construct the selector of this ItemLoader, if only a response is given in the constructor. If a selector is given in the constructor this attribute is ignored. This attribute is sometimes overridden in subclasses.

selector

The Selector object to extract data from. It’s either the selector given in the constructor or one created from the response given in the constructor using the default_selector_class. This attribute is meant to be read-only.

Reusing and extending Item Loaders

As your project grows bigger and acquires more and more spiders, maintenance becomes a fundamental problem, especially when you have to deal with many different parsing rules for each spider, having a lot of exceptions, but also wanting to reuse the common processors.

Item Loaders are designed to ease the maintenance burden of parsing rules, without losing flexibility and, at the same time, providing a convenient mechanism for extending and overriding them. For this reason Item Loaders support traditional Python class inheritance for dealing with differences of specific spiders (or groups of spiders).

Suppose, for example, that some particular site encloses their product names in three dashes (e.g. ---Plasma TV---) and you don’t want to end up scraping those dashes in the final product names.

Here’s how you can remove those dashes by reusing and extending the default Product Item Loader (ProductLoader):

from scrapy.loader.processors import MapCompose
from myproject.ItemLoaders import ProductLoader

def strip_dashes(x):
    return x.strip('-')

class SiteSpecificLoader(ProductLoader):
    name_in = MapCompose(strip_dashes, ProductLoader.name_in)

Another case where extending Item Loaders can be very helpful is when you have multiple source formats, for example XML and HTML. In the XML version you may want to remove CDATA occurrences. Here’s an example of how to do it:

from scrapy.loader.processors import MapCompose
from myproject.ItemLoaders import ProductLoader
from myproject.utils.xml import remove_cdata

class XmlProductLoader(ProductLoader):
    name_in = MapCompose(remove_cdata, ProductLoader.name_in)

And that’s how you typically extend input processors.

As for output processors, it is more common to declare them in the field metadata, as they usually depend only on the field and not on each specific site parsing rule (as input processors do). See also: Declaring Input and Output Processors.

There are many other possible ways to extend, inherit and override your Item Loaders, and different Item Loaders hierarchies may fit better for different projects. Scrapy only provides the mechanism; it doesn’t impose any specific organization of your Loaders collection - that’s up to you and your project’s needs.

Available built-in processors

Even though you can use any callable function as input and output processors, Scrapy provides some commonly used processors, which are described below. Some of them, like the MapCompose (which is typically used as input processor) compose the output of several functions executed in order, to produce the final parsed value.

Here is a list of all built-in processors:

class scrapy.loader.processors.Identity

The simplest processor, which doesn’t do anything. It returns the original values unchanged. It doesn’t receive any constructor arguments, nor does it accept Loader contexts.

Example:

>>> from scrapy.loader.processors import Identity
>>> proc = Identity()
>>> proc(['one', 'two', 'three'])
['one', 'two', 'three']
class scrapy.loader.processors.TakeFirst

Returns the first non-null/non-empty value from the values received, so it’s typically used as an output processor to single-valued fields. It doesn’t receive any constructor arguments, nor does it accept Loader contexts.

Example:

>>> from scrapy.loader.processors import TakeFirst
>>> proc = TakeFirst()
>>> proc(['', 'one', 'two', 'three'])
'one'
class scrapy.loader.processors.Join(separator=u' ')

Returns the values joined with the separator given in the constructor, which defaults to u' '. It doesn’t accept Loader contexts.

When using the default separator, this processor is equivalent to the function: u' '.join

Examples:

>>> from scrapy.loader.processors import Join
>>> proc = Join()
>>> proc(['one', 'two', 'three'])
u'one two three'
>>> proc = Join('<br>')
>>> proc(['one', 'two', 'three'])
u'one<br>two<br>three'
class scrapy.loader.processors.Compose(*functions, **default_loader_context)

A processor which is constructed from the composition of the given functions. This means that each input value of this processor is passed to the first function, and the result of that function is passed to the second function, and so on, until the last function returns the output value of this processor.

By default, stop process on None value. This behaviour can be changed by passing keyword argument stop_on_none=False.

Example:

>>> from scrapy.loader.processors import Compose
>>> proc = Compose(lambda v: v[0], str.upper)
>>> proc(['hello', 'world'])
'HELLO'

Each function can optionally receive a loader_context parameter. For those which do, this processor will pass the currently active Loader context through that parameter.

The keyword arguments passed in the constructor are used as the default Loader context values passed to each function call. However, the final Loader context values passed to functions are overridden with the currently active Loader context accessible through the ItemLoader.context() attribute.

class scrapy.loader.processors.MapCompose(*functions, **default_loader_context)

A processor which is constructed from the composition of the given functions, similar to the Compose processor. The difference with this processor is the way internal results are passed among functions, which is as follows:

The input value of this processor is iterated and the first function is applied to each element. The results of these function calls (one for each element) are concatenated to construct a new iterable, which is then used to apply the second function, and so on, until the last function is applied to each value of the list of values collected so far. The output values of the last function are concatenated together to produce the output of this processor.

Each particular function can return a value or a list of values, which is flattened with the list of values returned by the same function applied to the other input values. The functions can also return None in which case the output of that function is ignored for further processing over the chain.

This processor provides a convenient way to compose functions that only work with single values (instead of iterables). For this reason the MapCompose processor is typically used as input processor, since data is often extracted using the extract() method of selectors, which returns a list of unicode strings.

The example below should clarify how it works:

>>> def filter_world(x):
...     return None if x == 'world' else x
...
>>> from scrapy.loader.processors import MapCompose
>>> proc = MapCompose(filter_world, unicode.upper)
>>> proc([u'hello', u'world', u'this', u'is', u'scrapy'])
[u'HELLO, u'THIS', u'IS', u'SCRAPY']

As with the Compose processor, functions can receive Loader contexts, and constructor keyword arguments are used as default context values. See Compose processor for more info.

class scrapy.loader.processors.SelectJmes(json_path)

Queries the value using the json path provided to the constructor and returns the output. Requires jmespath (https://github.com/jmespath/jmespath.py) to run. This processor takes only one input at a time.

Example:

>>> from scrapy.loader.processors import SelectJmes, Compose, MapCompose
>>> proc = SelectJmes("foo") #for direct use on lists and dictionaries
>>> proc({'foo': 'bar'})
'bar'
>>> proc({'foo': {'bar': 'baz'}})
{'bar': 'baz'}

Working with Json:

>>> import json
>>> proc_single_json_str = Compose(json.loads, SelectJmes("foo"))
>>> proc_single_json_str('{"foo": "bar"}')
u'bar'
>>> proc_json_list = Compose(json.loads, MapCompose(SelectJmes('foo')))
>>> proc_json_list('[{"foo":"bar"}, {"baz":"tar"}]')
[u'bar']

Scrapy shell

The Scrapy shell is an interactive shell where you can try and debug your scraping code very quickly, without having to run the spider. It’s meant to be used for testing data extraction code, but you can actually use it for testing any kind of code as it is also a regular Python shell.

The shell is used for testing XPath or CSS expressions and see how they work and what data they extract from the web pages you’re trying to scrape. It allows you to interactively test your expressions while you’re writing your spider, without having to run the spider to test every change.

Once you get familiarized with the Scrapy shell, you’ll see that it’s an invaluable tool for developing and debugging your spiders.

If you have IPython installed, the Scrapy shell will use it (instead of the standard Python console). The IPython console is much more powerful and provides smart auto-completion and colorized output, among other things.

We highly recommend you install IPython, specially if you’re working on Unix systems (where IPython excels). See the IPython installation guide for more info.

Launch the shell

To launch the Scrapy shell you can use the shell command like this:

scrapy shell <url>

Where the <url> is the URL you want to scrape.

Using the shell

The Scrapy shell is just a regular Python console (or IPython console if you have it available) which provides some additional shortcut functions for convenience.

Available Shortcuts
  • shelp() - print a help with the list of available objects and shortcuts
  • fetch(request_or_url) - fetch a new response from the given request or URL and update all related objects accordingly.
  • view(response) - open the given response in your local web browser, for inspection. This will add a <base> tag to the response body in order for external links (such as images and style sheets) to display properly. Note, however, that this will create a temporary file in your computer, which won’t be removed automatically.
Available Scrapy objects

The Scrapy shell automatically creates some convenient objects from the downloaded page, like the Response object and the Selector objects (for both HTML and XML content).

Those objects are:

  • crawler - the current Crawler object.
  • spider - the Spider which is known to handle the URL, or a Spider object if there is no spider found for the current URL
  • request - a Request object of the last fetched page. You can modify this request using replace() or fetch a new request (without leaving the shell) using the fetch shortcut.
  • response - a Response object containing the last fetched page
  • settings - the current Scrapy settings

Example of shell session

Here’s an example of a typical shell session where we start by scraping the http://scrapy.org page, and then proceed to scrape the http://slashdot.org page. Finally, we modify the (Slashdot) request method to POST and re-fetch it getting a HTTP 405 (method not allowed) error. We end the session by typing Ctrl-D (in Unix systems) or Ctrl-Z in Windows.

Keep in mind that the data extracted here may not be the same when you try it, as those pages are not static and could have changed by the time you test this. The only purpose of this example is to get you familiarized with how the Scrapy shell works.

First, we launch the shell:

scrapy shell 'http://scrapy.org' --nolog

Then, the shell fetches the URL (using the Scrapy downloader) and prints the list of available objects and useful shortcuts (you’ll notice that these lines all start with the [s] prefix):

[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x1e16b50>
[s]   item       {}
[s]   request    <GET http://scrapy.org>
[s]   response   <200 http://scrapy.org>
[s]   settings   <scrapy.settings.Settings object at 0x2bfd650>
[s]   spider     <Spider 'default' at 0x20c6f50>
[s] Useful shortcuts:
[s]   shelp()           Shell help (print this help)
[s]   fetch(req_or_url) Fetch request (or URL) and update local objects
[s]   view(response)    View response in a browser

>>>

After that, we can start playing with the objects:

>>> response.xpath("//h1/text()").extract()[0]
u'Meet Scrapy'

>>> fetch("http://slashdot.org")
[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x1a13b50>
[s]   item       {}
[s]   request    <GET http://slashdot.org>
[s]   response   <200 http://slashdot.org>
[s]   settings   <scrapy.settings.Settings object at 0x2bfd650>
[s]   spider     <Spider 'default' at 0x20c6f50>
[s] Useful shortcuts:
[s]   shelp()           Shell help (print this help)
[s]   fetch(req_or_url) Fetch request (or URL) and update local objects
[s]   view(response)    View response in a browser

>>> response.xpath('//title/text()').extract()
[u'Slashdot: News for nerds, stuff that matters']

>>> request = request.replace(method="POST")

>>> fetch(request)
[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x1e16b50>
...

>>>

Invoking the shell from spiders to inspect responses

Sometimes you want to inspect the responses that are being processed in a certain point of your spider, if only to check that response you expect is getting there.

This can be achieved by using the scrapy.shell.inspect_response function.

Here’s an example of how you would call it from your spider:

import scrapy


class MySpider(scrapy.Spider):
    name = "myspider"
    start_urls = [
        "http://example.com",
        "http://example.org",
        "http://example.net",
    ]

    def parse(self, response):
        # We want to inspect one specific response.
        if ".org" in response.url:
            from scrapy.shell import inspect_response
            inspect_response(response, self)

        # Rest of parsing code.

When you run the spider, you will get something similar to this:

2014-01-23 17:48:31-0400 [scrapy] DEBUG: Crawled (200) <GET http://example.com> (referer: None)
2014-01-23 17:48:31-0400 [scrapy] DEBUG: Crawled (200) <GET http://example.org> (referer: None)
[s] Available Scrapy objects:
[s]   crawler    <scrapy.crawler.Crawler object at 0x1e16b50>
...

>>> response.url
'http://example.org'

Then, you can check if the extraction code is working:

>>> response.xpath('//h1[@class="fn"]')
[]

Nope, it doesn’t. So you can open the response in your web browser and see if it’s the response you were expecting:

>>> view(response)
True

Finally you hit Ctrl-D (or Ctrl-Z in Windows) to exit the shell and resume the crawling:

>>> ^D
2014-01-23 17:50:03-0400 [scrapy] DEBUG: Crawled (200) <GET http://example.net> (referer: None)
...

Note that you can’t use the fetch shortcut here since the Scrapy engine is blocked by the shell. However, after you leave the shell, the spider will continue crawling where it stopped, as shown above.

Item Pipeline

After an item has been scraped by a spider, it is sent to the Item Pipeline which processes it through several components that are executed sequentially.

Each item pipeline component (sometimes referred as just “Item Pipeline”) is a Python class that implements a simple method. They receive an item and perform an action over it, also deciding if the item should continue through the pipeline or be dropped and no longer processed.

Typical uses of item pipelines are:

  • cleansing HTML data
  • validating scraped data (checking that the items contain certain fields)
  • checking for duplicates (and dropping them)
  • storing the scraped item in a database

Writing your own item pipeline

Each item pipeline component is a Python class that must implement the following method:

process_item(self, item, spider)

This method is called for every item pipeline component and must either return a dict with data, Item (or any descendant class) object or raise a DropItem exception. Dropped items are no longer processed by further pipeline components.

Parameters:
  • item (Item object or a dict) – the item scraped
  • spider (Spider object) – the spider which scraped the item

Additionally, they may also implement the following methods:

open_spider(self, spider)

This method is called when the spider is opened.

Parameters:spider (Spider object) – the spider which was opened
close_spider(self, spider)

This method is called when the spider is closed.

Parameters:spider (Spider object) – the spider which was closed
from_crawler(cls, crawler)

If present, this classmethod is called to create a pipeline instance from a Crawler. It must return a new instance of the pipeline. Crawler object provides access to all Scrapy core components like settings and signals; it is a way for pipeline to access them and hook its functionality into Scrapy.

Parameters:crawler (Crawler object) – crawler that uses this pipeline

Item pipeline example

Price validation and dropping items with no prices

Let’s take a look at the following hypothetical pipeline that adjusts the price attribute for those items that do not include VAT (price_excludes_vat attribute), and drops those items which don’t contain a price:

from scrapy.exceptions import DropItem

class PricePipeline(object):

    vat_factor = 1.15

    def process_item(self, item, spider):
        if item['price']:
            if item['price_excludes_vat']:
                item['price'] = item['price'] * self.vat_factor
            return item
        else:
            raise DropItem("Missing price in %s" % item)
Write items to a JSON file

The following pipeline stores all scraped items (from all spiders) into a single items.jl file, containing one item per line serialized in JSON format:

import json

class JsonWriterPipeline(object):

    def __init__(self):
        self.file = open('items.jl', 'wb')

    def process_item(self, item, spider):
        line = json.dumps(dict(item)) + "\n"
        self.file.write(line)
        return item

Note

The purpose of JsonWriterPipeline is just to introduce how to write item pipelines. If you really want to store all scraped items into a JSON file you should use the Feed exports.

Write items to MongoDB

In this example we’ll write items to MongoDB using pymongo. MongoDB address and database name are specified in Scrapy settings; MongoDB collection is named after item class.

The main point of this example is to show how to use from_crawler() method and how to clean up the resources properly.

Note

Previous example (JsonWriterPipeline) doesn’t clean up resources properly. Fixing it is left as an exercise for the reader.

import pymongo

class MongoPipeline(object):

    collection_name = 'scrapy_items'

    def __init__(self, mongo_uri, mongo_db):
        self.mongo_uri = mongo_uri
        self.mongo_db = mongo_db

    @classmethod
    def from_crawler(cls, crawler):
        return cls(
            mongo_uri=crawler.settings.get('MONGO_URI'),
            mongo_db=crawler.settings.get('MONGO_DATABASE', 'items')
        )

    def open_spider(self, spider):
        self.client = pymongo.MongoClient(self.mongo_uri)
        self.db = self.client[self.mongo_db]

    def close_spider(self, spider):
        self.client.close()

    def process_item(self, item, spider):
        self.db[self.collection_name].insert(dict(item))
        return item
Duplicates filter

A filter that looks for duplicate items, and drops those items that were already processed. Let’s say that our items have a unique id, but our spider returns multiples items with the same id:

from scrapy.exceptions import DropItem

class DuplicatesPipeline(object):

    def __init__(self):
        self.ids_seen = set()

    def process_item(self, item, spider):
        if item['id'] in self.ids_seen:
            raise DropItem("Duplicate item found: %s" % item)
        else:
            self.ids_seen.add(item['id'])
            return item

Activating an Item Pipeline component

To activate an Item Pipeline component you must add its class to the ITEM_PIPELINES setting, like in the following example:

ITEM_PIPELINES = {
    'myproject.pipelines.PricePipeline': 300,
    'myproject.pipelines.JsonWriterPipeline': 800,
}

The integer values you assign to classes in this setting determine the order in which they run: items go through from lower valued to higher valued classes. It’s customary to define these numbers in the 0-1000 range.

Feed exports

New in version 0.10.

One of the most frequently required features when implementing scrapers is being able to store the scraped data properly and, quite often, that means generating an “export file” with the scraped data (commonly called “export feed”) to be consumed by other systems.

Scrapy provides this functionality out of the box with the Feed Exports, which allows you to generate a feed with the scraped items, using multiple serialization formats and storage backends.

Serialization formats

For serializing the scraped data, the feed exports use the Item exporters. These formats are supported out of the box:

But you can also extend the supported format through the FEED_EXPORTERS setting.

JSON
JSON lines
CSV
  • FEED_FORMAT: csv
  • Exporter used: CsvItemExporter
  • To specify columns to export and their order use FEED_EXPORT_FIELDS. Other feed exporters can also use this option, but it is important for CSV because unlike many other export formats CSV uses a fixed header.
XML
Pickle
Marshal
  • FEED_FORMAT: marshal
  • Exporter used: MarshalItemExporter

Storages

When using the feed exports you define where to store the feed using a URI (through the FEED_URI setting). The feed exports supports multiple storage backend types which are defined by the URI scheme.

The storages backends supported out of the box are:

Some storage backends may be unavailable if the required external libraries are not available. For example, the S3 backend is only available if the boto library is installed.

Storage URI parameters

The storage URI can also contain parameters that get replaced when the feed is being created. These parameters are:

  • %(time)s - gets replaced by a timestamp when the feed is being created
  • %(name)s - gets replaced by the spider name

Any other named parameter gets replaced by the spider attribute of the same name. For example, %(site_id)s would get replaced by the spider.site_id attribute the moment the feed is being created.

Here are some examples to illustrate:

  • Store in FTP using one directory per spider:
    • ftp://user:password@ftp.example.com/scraping/feeds/%(name)s/%(time)s.json
  • Store in S3 using one directory per spider:
    • s3://mybucket/scraping/feeds/%(name)s/%(time)s.json

Storage backends

Local filesystem

The feeds are stored in the local filesystem.

  • URI scheme: file
  • Example URI: file:///tmp/export.csv
  • Required external libraries: none

Note that for the local filesystem storage (only) you can omit the scheme if you specify an absolute path like /tmp/export.csv. This only works on Unix systems though.

FTP

The feeds are stored in a FTP server.

  • URI scheme: ftp
  • Example URI: ftp://user:pass@ftp.example.com/path/to/export.csv
  • Required external libraries: none
S3

The feeds are stored on Amazon S3.

  • URI scheme: s3
  • Example URIs:
    • s3://mybucket/path/to/export.csv
    • s3://aws_key:aws_secret@mybucket/path/to/export.csv
  • Required external libraries: boto

The AWS credentials can be passed as user/password in the URI, or they can be passed through the following settings:

Standard output

The feeds are written to the standard output of the Scrapy process.

  • URI scheme: stdout
  • Example URI: stdout:
  • Required external libraries: none

Settings

These are the settings used for configuring the feed exports:

FEED_URI

Default: None

The URI of the export feed. See Storage backends for supported URI schemes.

This setting is required for enabling the feed exports.

FEED_FORMAT

The serialization format to be used for the feed. See Serialization formats for possible values.

FEED_EXPORT_FIELDS

Default: None

A list of fields to export, optional. Example: FEED_EXPORT_FIELDS = ["foo", "bar", "baz"].

Use FEED_EXPORT_FIELDS option to define fields to export and their order.

When FEED_EXPORT_FIELDS is empty or None (default), Scrapy uses fields defined in dicts or Item subclasses a spider is yielding.

If an exporter requires a fixed set of fields (this is the case for CSV export format) and FEED_EXPORT_FIELDS is empty or None, then Scrapy tries to infer field names from the exported data - currently it uses field names from the first item.

FEED_STORE_EMPTY

Default: False

Whether to export empty feeds (ie. feeds with no items).

FEED_STORAGES

Default:: {}

A dict containing additional feed storage backends supported by your project. The keys are URI schemes and the values are paths to storage classes.

FEED_STORAGES_BASE

Default:

{
    '': 'scrapy.extensions.feedexport.FileFeedStorage',
    'file': 'scrapy.extensions.feedexport.FileFeedStorage',
    'stdout': 'scrapy.extensions.feedexport.StdoutFeedStorage',
    's3': 'scrapy.extensions.feedexport.S3FeedStorage',
    'ftp': 'scrapy.extensions.feedexport.FTPFeedStorage',
}

A dict containing the built-in feed storage backends supported by Scrapy.

FEED_EXPORTERS

Default:: {}

A dict containing additional exporters supported by your project. The keys are URI schemes and the values are paths to Item exporter classes.

FEED_EXPORTERS_BASE

Default:

FEED_EXPORTERS_BASE = {
    'json': 'scrapy.exporters.JsonItemExporter',
    'jsonlines': 'scrapy.exporters.JsonLinesItemExporter',
    'csv': 'scrapy.exporters.CsvItemExporter',
    'xml': 'scrapy.exporters.XmlItemExporter',
    'marshal': 'scrapy.exporters.MarshalItemExporter',
}

A dict containing the built-in feed exporters supported by Scrapy.

Requests and Responses

Scrapy uses Request and Response objects for crawling web sites.

Typically, Request objects are generated in the spiders and pass across the system until they reach the Downloader, which executes the request and returns a Response object which travels back to the spider that issued the request.

Both Request and Response classes have subclasses which add functionality not required in the base classes. These are described below in Request subclasses and Response subclasses.

Request objects

class scrapy.http.Request(url[, callback, method='GET', headers, body, cookies, meta, encoding='utf-8', priority=0, dont_filter=False, errback])

A Request object represents an HTTP request, which is usually generated in the Spider and executed by the Downloader, and thus generating a Response.

Parameters:
  • url (string) – the URL of this request
  • callback (callable) – the function that will be called with the response of this request (once its downloaded) as its first parameter. For more information see Passing additional data to callback functions below. If a Request doesn’t specify a callback, the spider’s parse() method will be used. Note that if exceptions are raised during processing, errback is called instead.
  • method (string) – the HTTP method of this request. Defaults to 'GET'.
  • meta (dict) – the initial values for the Request.meta attribute. If given, the dict passed in this parameter will be shallow copied.
  • body (str or unicode) – the request body. If a unicode is passed, then it’s encoded to str using the encoding passed (which defaults to utf-8). If body is not given, an empty string is stored. Regardless of the type of this argument, the final value stored will be a str (never unicode or None).
  • headers (dict) – the headers of this request. The dict values can be strings (for single valued headers) or lists (for multi-valued headers). If None is passed as value, the HTTP header will not be sent at all.
  • cookies (dict or list) –

    the request cookies. These can be sent in two forms.

    1. Using a dict:
      request_with_cookies = Request(url="http://www.example.com",
                               cookies={'currency': 'USD', 'country': 'UY'})
    2. Using a list of dicts:
      request_with_cookies = Request(url="http://www.example.com",
                               cookies=[{'name': 'currency',
                                        'value': 'USD',
                                        'domain': 'example.com',
                                        'path': '/currency'}])

    The latter form allows for customizing the domain and path attributes of the cookie. This is only useful if the cookies are saved for later requests.

    When some site returns cookies (in a response) those are stored in the cookies for that domain and will be sent again in future requests. That’s the typical behaviour of any regular web browser. However, if, for some reason, you want to avoid merging with existing cookies you can instruct Scrapy to do so by setting the dont_merge_cookies key to True in the Request.meta.

    Example of request without merging cookies:

    request_with_cookies = Request(url="http://www.example.com",
                               cookies={'currency': 'USD', 'country': 'UY'},
                               meta={'dont_merge_cookies': True})

    For more info see CookiesMiddleware.

  • encoding (string) – the encoding of this request (defaults to 'utf-8'). This encoding will be used to percent-encode the URL and to convert the body to str (if given as unicode).
  • priority (int) – the priority of this request (defaults to 0). The priority is used by the scheduler to define the order used to process requests. Requests with a higher priority value will execute earlier. Negative values are allowed in order to indicate relatively low-priority.
  • dont_filter (boolean) – indicates that this request should not be filtered by the scheduler. This is used when you want to perform an identical request multiple times, to ignore the duplicates filter. Use it with care, or you will get into crawling loops. Default to False.
  • errback (callable) – a function that will be called if any exception was raised while processing the request. This includes pages that failed with 404 HTTP errors and such. It receives a Twisted Failure instance as first parameter.
url

A string containing the URL of this request. Keep in mind that this attribute contains the escaped URL, so it can differ from the URL passed in the constructor.

This attribute is read-only. To change the URL of a Request use replace().

method

A string representing the HTTP method in the request. This is guaranteed to be uppercase. Example: "GET", "POST", "PUT", etc

headers

A dictionary-like object which contains the request headers.

body

A str that contains the request body.

This attribute is read-only. To change the body of a Request use replace().

meta

A dict that contains arbitrary metadata for this request. This dict is empty for new Requests, and is usually populated by different Scrapy components (extensions, middlewares, etc). So the data contained in this dict depends on the extensions you have enabled.

See Request.meta special keys for a list of special meta keys recognized by Scrapy.

This dict is shallow copied when the request is cloned using the copy() or replace() methods, and can also be accessed, in your spider, from the response.meta attribute.

copy()

Return a new Request which is a copy of this Request. See also: Passing additional data to callback functions.

replace([url, method, headers, body, cookies, meta, encoding, dont_filter, callback, errback])

Return a Request object with the same members, except for those members given new values by whichever keyword arguments are specified. The attribute Request.meta is copied by default (unless a new value is given in the meta argument). See also Passing additional data to callback functions.

Passing additional data to callback functions

The callback of a request is a function that will be called when the response of that request is downloaded. The callback function will be called with the downloaded Response object as its first argument.

Example:

def parse_page1(self, response):
    return scrapy.Request("http://www.example.com/some_page.html",
                          callback=self.parse_page2)

def parse_page2(self, response):
    # this would log http://www.example.com/some_page.html
    self.logger.info("Visited %s", response.url)

In some cases you may be interested in passing arguments to those callback functions so you can receive the arguments later, in the second callback. You can use the Request.meta attribute for that.

Here’s an example of how to pass an item using this mechanism, to populate different fields from different pages:

def parse_page1(self, response):
    item = MyItem()
    item['main_url'] = response.url
    request = scrapy.Request("http://www.example.com/some_page.html",
                             callback=self.parse_page2)
    request.meta['item'] = item
    return request

def parse_page2(self, response):
    item = response.meta['item']
    item['other_url'] = response.url
    return item

Request.meta special keys

The Request.meta attribute can contain any arbitrary data, but there are some special keys recognized by Scrapy and its built-in extensions.

Those are:

bindaddress

The IP of the outgoing IP address to use for the performing the request.

download_timeout

The amount of time (in secs) that the downloader will wait before timing out. See also: DOWNLOAD_TIMEOUT.

Request subclasses

Here is the list of built-in Request subclasses. You can also subclass it to implement your own custom functionality.

FormRequest objects

The FormRequest class extends the base Request with functionality for dealing with HTML forms. It uses lxml.html forms to pre-populate form fields with form data from Response objects.

class scrapy.http.FormRequest(url[, formdata, ...])

The FormRequest class adds a new argument to the constructor. The remaining arguments are the same as for the Request class and are not documented here.

Parameters:formdata (dict or iterable of tuples) – is a dictionary (or iterable of (key, value) tuples) containing HTML Form data which will be url-encoded and assigned to the body of the request.

The FormRequest objects support the following class method in addition to the standard Request methods:

classmethod from_response(response[, formname=None, formnumber=0, formdata=None, formxpath=None, clickdata=None, dont_click=False, ...])

Returns a new FormRequest object with its form field values pre-populated with those found in the HTML <form> element contained in the given response. For an example see Using FormRequest.from_response() to simulate a user login.

The policy is to automatically simulate a click, by default, on any form control that looks clickable, like a <input type="submit">. Even though this is quite convenient, and often the desired behaviour, sometimes it can cause problems which could be hard to debug. For example, when working with forms that are filled and/or submitted using javascript, the default from_response() behaviour may not be the most appropriate. To disable this behaviour you can set the dont_click argument to True. Also, if you want to change the control clicked (instead of disabling it) you can also use the clickdata argument.

Parameters:
  • response (Response object) – the response containing a HTML form which will be used to pre-populate the form fields
  • formname (string) – if given, the form with name attribute set to this value will be used.
  • formxpath (string) – if given, the first form that matches the xpath will be used.
  • formnumber (integer) – the number of form to use, when the response contains multiple forms. The first one (and also the default) is 0.
  • formdata (dict) – fields to override in the form data. If a field was already present in the response <form> element, its value is overridden by the one passed in this parameter.
  • clickdata (dict) – attributes to lookup the control clicked. If it’s not given, the form data will be submitted simulating a click on the first clickable element. In addition to html attributes, the control can be identified by its zero-based index relative to other submittable inputs inside the form, via the nr attribute.
  • dont_click (boolean) – If True, the form data will be submitted without clicking in any element.

The other parameters of this class method are passed directly to the FormRequest constructor.

New in version 0.10.3: The formname parameter.

New in version 0.17: The formxpath parameter.

Request usage examples
Using FormRequest to send data via HTTP POST

If you want to simulate a HTML Form POST in your spider and send a couple of key-value fields, you can return a FormRequest object (from your spider) like this:

return [FormRequest(url="http://www.example.com/post/action",
                    formdata={'name': 'John Doe', 'age': '27'},
                    callback=self.after_post)]
Using FormRequest.from_response() to simulate a user login

It is usual for web sites to provide pre-populated form fields through <input type="hidden"> elements, such as session related data or authentication tokens (for login pages). When scraping, you’ll want these fields to be automatically pre-populated and only override a couple of them, such as the user name and password. You can use the FormRequest.from_response() method for this job. Here’s an example spider which uses it:

import scrapy

class LoginSpider(scrapy.Spider):
    name = 'example.com'
    start_urls = ['http://www.example.com/users/login.php']

    def parse(self, response):
        return scrapy.FormRequest.from_response(
            response,
            formdata={'username': 'john', 'password': 'secret'},
            callback=self.after_login
        )

    def after_login(self, response):
        # check login succeed before going on
        if "authentication failed" in response.body:
            self.logger.error("Login failed")
            return

        # continue scraping with authenticated session...

Response objects

class scrapy.http.Response(url[, status=200, headers, body, flags])

A Response object represents an HTTP response, which is usually downloaded (by the Downloader) and fed to the Spiders for processing.

Parameters:
  • url (string) – the URL of this response
  • headers (dict) – the headers of this response. The dict values can be strings (for single valued headers) or lists (for multi-valued headers).
  • status (integer) – the HTTP status of the response. Defaults to 200.
  • body (str) – the response body. It must be str, not unicode, unless you’re using a encoding-aware Response subclass, such as TextResponse.
  • meta (dict) – the initial values for the Response.meta attribute. If given, the dict will be shallow copied.
  • flags (list) – is a list containing the initial values for the Response.flags attribute. If given, the list will be shallow copied.
url

A string containing the URL of the response.

This attribute is read-only. To change the URL of a Response use replace().

status

An integer representing the HTTP status of the response. Example: 200, 404.

headers

A dictionary-like object which contains the response headers.

body

A str containing the body of this Response. Keep in mind that Response.body is always a str. If you want the unicode version use TextResponse.body_as_unicode() (only available in TextResponse and subclasses).

This attribute is read-only. To change the body of a Response use replace().

request

The Request object that generated this response. This attribute is assigned in the Scrapy engine, after the response and the request have passed through all Downloader Middlewares. In particular, this means that:

  • HTTP redirections will cause the original request (to the URL before redirection) to be assigned to the redirected response (with the final URL after redirection).
  • Response.request.url doesn’t always equal Response.url
  • This attribute is only available in the spider code, and in the Spider Middlewares, but not in Downloader Middlewares (although you have the Request available there by other means) and handlers of the response_downloaded signal.
meta

A shortcut to the Request.meta attribute of the Response.request object (ie. self.request.meta).

Unlike the Response.request attribute, the Response.meta attribute is propagated along redirects and retries, so you will get the original Request.meta sent from your spider.

See also

Request.meta attribute

flags

A list that contains flags for this response. Flags are labels used for tagging Responses. For example: ‘cached’, ‘redirected‘, etc. And they’re shown on the string representation of the Response (__str__ method) which is used by the engine for logging.

copy()

Returns a new Response which is a copy of this Response.

replace([url, status, headers, body, request, flags, cls])

Returns a Response object with the same members, except for those members given new values by whichever keyword arguments are specified. The attribute Response.meta is copied by default.

urljoin(url)

Constructs an absolute url by combining the Response’s url with a possible relative url.

This is a wrapper over urlparse.urljoin, it’s merely an alias for making this call:

urlparse.urljoin(response.url, url)

Response subclasses

Here is the list of available built-in Response subclasses. You can also subclass the Response class to implement your own functionality.

TextResponse objects
class scrapy.http.TextResponse(url[, encoding[, ...]])

TextResponse objects adds encoding capabilities to the base Response class, which is meant to be used only for binary data, such as images, sounds or any media file.

TextResponse objects support a new constructor argument, in addition to the base Response objects. The remaining functionality is the same as for the Response class and is not documented here.

Parameters:encoding (string) – is a string which contains the encoding to use for this response. If you create a TextResponse object with a unicode body, it will be encoded using this encoding (remember the body attribute is always a string). If encoding is None (default value), the encoding will be looked up in the response headers and body instead.

TextResponse objects support the following attributes in addition to the standard Response ones:

encoding

A string with the encoding of this response. The encoding is resolved by trying the following mechanisms, in order:

  1. the encoding passed in the constructor encoding argument
  2. the encoding declared in the Content-Type HTTP header. If this encoding is not valid (ie. unknown), it is ignored and the next resolution mechanism is tried.
  3. the encoding declared in the response body. The TextResponse class doesn’t provide any special functionality for this. However, the HtmlResponse and XmlResponse classes do.
  4. the encoding inferred by looking at the response body. This is the more fragile method but also the last one tried.
selector

A Selector instance using the response as target. The selector is lazily instantiated on first access.

TextResponse objects support the following methods in addition to the standard Response ones:

body_as_unicode()

Returns the body of the response as unicode. This is equivalent to:

response.body.decode(response.encoding)

But not equivalent to:

unicode(response.body)

Since, in the latter case, you would be using the system default encoding (typically ascii) to convert the body to unicode, instead of the response encoding.

xpath(query)

A shortcut to TextResponse.selector.xpath(query):

response.xpath('//p')
css(query)

A shortcut to TextResponse.selector.css(query):

response.css('p')
HtmlResponse objects
class scrapy.http.HtmlResponse(url[, ...])

The HtmlResponse class is a subclass of TextResponse which adds encoding auto-discovering support by looking into the HTML meta http-equiv attribute. See TextResponse.encoding.

XmlResponse objects
class scrapy.http.XmlResponse(url[, ...])

The XmlResponse class is a subclass of TextResponse which adds encoding auto-discovering support by looking into the XML declaration line. See TextResponse.encoding.

Settings

The Scrapy settings allows you to customize the behaviour of all Scrapy components, including the core, extensions, pipelines and spiders themselves.

The infrastructure of the settings provides a global namespace of key-value mappings that the code can use to pull configuration values from. The settings can be populated through different mechanisms, which are described below.

The settings are also the mechanism for selecting the currently active Scrapy project (in case you have many).

For a list of available built-in settings see: Built-in settings reference.

Designating the settings

When you use Scrapy, you have to tell it which settings you’re using. You can do this by using an environment variable, SCRAPY_SETTINGS_MODULE.

The value of SCRAPY_SETTINGS_MODULE should be in Python path syntax, e.g. myproject.settings. Note that the settings module should be on the Python import search path.

Populating the settings

Settings can be populated using different mechanisms, each of which having a different precedence. Here is the list of them in decreasing order of precedence:

  1. Command line options (most precedence)
  2. Settings per-spider
  3. Project settings module
  4. Default settings per-command
  5. Default global settings (less precedence)

The population of these settings sources is taken care of internally, but a manual handling is possible using API calls. See the Settings API topic for reference.

These mechanisms are described in more detail below.

1. Command line options

Arguments provided by the command line are the ones that take most precedence, overriding any other options. You can explicitly override one (or more) settings using the -s (or --set) command line option.

Example:

scrapy crawl myspider -s LOG_FILE=scrapy.log
2. Settings per-spider

Spiders (See the Spiders chapter for reference) can define their own settings that will take precedence and override the project ones. They can do so by setting their scrapy.spiders.Spider.custom_settings attribute.

3. Project settings module

The project settings module is the standard configuration file for your Scrapy project. It’s where most of your custom settings will be populated. For example:: myproject.settings.

4. Default settings per-command

Each Scrapy tool command can have its own default settings, which override the global default settings. Those custom command settings are specified in the default_settings attribute of the command class.

5. Default global settings

The global defaults are located in the scrapy.settings.default_settings module and documented in the Built-in settings reference section.

How to access settings

Settings can be accessed through the scrapy.crawler.Crawler.settings attribute of the Crawler that is passed to from_crawler method in extensions and middlewares:

class MyExtension(object):

    @classmethod
    def from_crawler(cls, crawler):
        settings = crawler.settings
        if settings['LOG_ENABLED']:
            print "log is enabled!"

In other words, settings can be accessed like a dict, but it’s usually preferred to extract the setting in the format you need it to avoid type errors. In order to do that you’ll have to use one of the methods provided the Settings API.

Rationale for setting names

Setting names are usually prefixed with the component that they configure. For example, proper setting names for a fictional robots.txt extension would be ROBOTSTXT_ENABLED, ROBOTSTXT_OBEY, ROBOTSTXT_CACHEDIR, etc.

Built-in settings reference

Here’s a list of all available Scrapy settings, in alphabetical order, along with their default values and the scope where they apply.

The scope, where available, shows where the setting is being used, if it’s tied to any particular component. In that case the module of that component will be shown, typically an extension, middleware or pipeline. It also means that the component must be enabled in order for the setting to have any effect.

AWS_ACCESS_KEY_ID

Default: None

The AWS access key used by code that requires access to Amazon Web services, such as the S3 feed storage backend.

AWS_SECRET_ACCESS_KEY

Default: None

The AWS secret key used by code that requires access to Amazon Web services, such as the S3 feed storage backend.

BOT_NAME

Default: 'scrapybot'

The name of the bot implemented by this Scrapy project (also known as the project name). This will be used to construct the User-Agent by default, and also for logging.

It’s automatically populated with your project name when you create your project with the startproject command.

CONCURRENT_ITEMS

Default: 100

Maximum number of concurrent items (per response) to process in parallel in the Item Processor (also known as the Item Pipeline).

CONCURRENT_REQUESTS

Default: 16

The maximum number of concurrent (ie. simultaneous) requests that will be performed by the Scrapy downloader.

CONCURRENT_REQUESTS_PER_DOMAIN

Default: 8

The maximum number of concurrent (ie. simultaneous) requests that will be performed to any single domain.

CONCURRENT_REQUESTS_PER_IP

Default: 0

The maximum number of concurrent (ie. simultaneous) requests that will be performed to any single IP. If non-zero, the CONCURRENT_REQUESTS_PER_DOMAIN setting is ignored, and this one is used instead. In other words, concurrency limits will be applied per IP, not per domain.

This setting also affects DOWNLOAD_DELAY: if CONCURRENT_REQUESTS_PER_IP is non-zero, download delay is enforced per IP, not per domain.

DEFAULT_ITEM_CLASS

Default: 'scrapy.item.Item'

The default class that will be used for instantiating items in the the Scrapy shell.

DEFAULT_REQUEST_HEADERS

Default:

{
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
    'Accept-Language': 'en',
}

The default headers used for Scrapy HTTP Requests. They’re populated in the DefaultHeadersMiddleware.

DEPTH_LIMIT

Default: 0

The maximum depth that will be allowed to crawl for any site. If zero, no limit will be imposed.

DEPTH_PRIORITY

Default: 0

An integer that is used to adjust the request priority based on its depth.

If zero, no priority adjustment is made from depth.

DEPTH_STATS

Default: True

Whether to collect maximum depth stats.

DEPTH_STATS_VERBOSE

Default: False

Whether to collect verbose depth stats. If this is enabled, the number of requests for each depth is collected in the stats.

DNSCACHE_ENABLED

Default: True

Whether to enable DNS in-memory cache.

DNSCACHE_SIZE

Default: 10000

DNS in-memory cache size.

DNS_TIMEOUT

Default: 60

Timeout for processing of DNS queries in seconds. Float is supported.

DOWNLOADER

Default: 'scrapy.core.downloader.Downloader'

The downloader to use for crawling.

DOWNLOADER_MIDDLEWARES

Default:: {}

A dict containing the downloader middlewares enabled in your project, and their orders. For more info see Activating a downloader middleware.

DOWNLOADER_MIDDLEWARES_BASE

Default:

{
    'scrapy.downloadermiddlewares.robotstxt.RobotsTxtMiddleware': 100,
    'scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware': 300,
    'scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware': 350,
    'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': 400,
    'scrapy.downloadermiddlewares.retry.RetryMiddleware': 500,
    'scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware': 550,
    'scrapy.downloadermiddlewares.ajaxcrawl.AjaxCrawlMiddleware': 560,
    'scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware': 580,
    'scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware': 590,
    'scrapy.downloadermiddlewares.redirect.RedirectMiddleware': 600,
    'scrapy.downloadermiddlewares.cookies.CookiesMiddleware': 700,
    'scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware': 750,
    'scrapy.downloadermiddlewares.chunked.ChunkedTransferMiddleware': 830,
    'scrapy.downloadermiddlewares.stats.DownloaderStats': 850,
    'scrapy.downloadermiddlewares.httpcache.HttpCacheMiddleware': 900,
}

A dict containing the downloader middlewares enabled by default in Scrapy. You should never modify this setting in your project, modify DOWNLOADER_MIDDLEWARES instead. For more info see Activating a downloader middleware.

DOWNLOADER_STATS

Default: True

Whether to enable downloader stats collection.

DOWNLOAD_DELAY

Default: 0

The amount of time (in secs) that the downloader should wait before downloading consecutive pages from the same website. This can be used to throttle the crawling speed to avoid hitting servers too hard. Decimal numbers are supported. Example:

DOWNLOAD_DELAY = 0.25    # 250 ms of delay

This setting is also affected by the RANDOMIZE_DOWNLOAD_DELAY setting (which is enabled by default). By default, Scrapy doesn’t wait a fixed amount of time between requests, but uses a random interval between 0.5 and 1.5 * DOWNLOAD_DELAY.

When CONCURRENT_REQUESTS_PER_IP is non-zero, delays are enforced per ip address instead of per domain.

You can also change this setting per spider by setting download_delay spider attribute.

DOWNLOAD_HANDLERS

Default: {}

A dict containing the request downloader handlers enabled in your project. See DOWNLOAD_HANDLERS_BASE for example format.

DOWNLOAD_HANDLERS_BASE

Default:

{
    'file': 'scrapy.core.downloader.handlers.file.FileDownloadHandler',
    'http': 'scrapy.core.downloader.handlers.http.HttpDownloadHandler',
    'https': 'scrapy.core.downloader.handlers.http.HttpDownloadHandler',
    's3': 'scrapy.core.downloader.handlers.s3.S3DownloadHandler',
}

A dict containing the request download handlers enabled by default in Scrapy. You should never modify this setting in your project, modify DOWNLOAD_HANDLERS instead.

If you want to disable any of the above download handlers you must define them in your project’s DOWNLOAD_HANDLERS setting and assign None as their value. For example, if you want to disable the file download handler:

DOWNLOAD_HANDLERS = {
    'file': None,
}
DOWNLOAD_TIMEOUT

Default: 180

The amount of time (in secs) that the downloader will wait before timing out.

Note

This timeout can be set per spider using download_timeout spider attribute and per-request using download_timeout Request.meta key.

DOWNLOAD_MAXSIZE

Default: 1073741824 (1024MB)

The maximum response size (in bytes) that downloader will download.

If you want to disable it set to 0.

Note

This size can be set per spider using download_maxsize spider attribute and per-request using download_maxsize Request.meta key.

This feature needs Twisted >= 11.1.

DOWNLOAD_WARNSIZE

Default: 33554432 (32MB)

The response size (in bytes) that downloader will start to warn.

If you want to disable it set to 0.

Note

This size can be set per spider using download_warnsize spider attribute and per-request using download_warnsize Request.meta key.

This feature needs Twisted >= 11.1.

DUPEFILTER_CLASS

Default: 'scrapy.dupefilters.RFPDupeFilter'

The class used to detect and filter duplicate requests.

The default (RFPDupeFilter) filters based on request fingerprint using the scrapy.utils.request.request_fingerprint function. In order to change the way duplicates are checked you could subclass RFPDupeFilter and override its request_fingerprint method. This method should accept scrapy Request object and return its fingerprint (a string).

DUPEFILTER_DEBUG

Default: False

By default, RFPDupeFilter only logs the first duplicate request. Setting DUPEFILTER_DEBUG to True will make it log all duplicate requests.

EDITOR

Default: depends on the environment

The editor to use for editing spiders with the edit command. It defaults to the EDITOR environment variable, if set. Otherwise, it defaults to vi (on Unix systems) or the IDLE editor (on Windows).

EXTENSIONS

Default:: {}

A dict containing the extensions enabled in your project, and their orders.

EXTENSIONS_BASE

Default:

{
    'scrapy.extensions.corestats.CoreStats': 0,
    'scrapy.telnet.TelnetConsole': 0,
    'scrapy.extensions.memusage.MemoryUsage': 0,
    'scrapy.extensions.memdebug.MemoryDebugger': 0,
    'scrapy.extensions.closespider.CloseSpider': 0,
    'scrapy.extensions.feedexport.FeedExporter': 0,
    'scrapy.extensions.logstats.LogStats': 0,
    'scrapy.extensions.spiderstate.SpiderState': 0,
    'scrapy.extensions.throttle.AutoThrottle': 0,
}

The list of available extensions. Keep in mind that some of them need to be enabled through a setting. By default, this setting contains all stable built-in extensions.

For more information See the extensions user guide and the list of available extensions.

ITEM_PIPELINES

Default: {}

A dict containing the item pipelines to use, and their orders. The dict is empty by default order values are arbitrary but it’s customary to define them in the 0-1000 range.

Lists are supported in ITEM_PIPELINES for backwards compatibility, but they are deprecated.

Example:

ITEM_PIPELINES = {
    'mybot.pipelines.validate.ValidateMyItem': 300,
    'mybot.pipelines.validate.StoreMyItem': 800,
}
ITEM_PIPELINES_BASE

Default: {}

A dict containing the pipelines enabled by default in Scrapy. You should never modify this setting in your project, modify ITEM_PIPELINES instead.

LOG_ENABLED

Default: True

Whether to enable logging.

LOG_ENCODING

Default: 'utf-8'

The encoding to use for logging.

LOG_FILE

Default: None

File name to use for logging output. If None, standard error will be used.

LOG_FORMAT

Default: '%(asctime)s [%(name)s] %(levelname)s: %(message)s'

String for formatting log messsages. Refer to the Python logging documentation for the whole list of available placeholders.

LOG_DATEFORMAT

Default: '%Y-%m-%d %H:%M:%S'

String for formatting date/time, expansion of the %(asctime)s placeholder in LOG_FORMAT. Refer to the Python datetime documentation for the whole list of available directives.

LOG_LEVEL

Default: 'DEBUG'

Minimum level to log. Available levels are: CRITICAL, ERROR, WARNING, INFO, DEBUG. For more info see Logging.

LOG_STDOUT

Default: False

If True, all standard output (and error) of your process will be redirected to the log. For example if you print 'hello' it will appear in the Scrapy log.

MEMDEBUG_ENABLED

Default: False

Whether to enable memory debugging.

MEMDEBUG_NOTIFY

Default: []

When memory debugging is enabled a memory report will be sent to the specified addresses if this setting is not empty, otherwise the report will be written to the log.

Example:

MEMDEBUG_NOTIFY = ['user@example.com']
MEMUSAGE_ENABLED

Default: False

Scope: scrapy.extensions.memusage

Whether to enable the memory usage extension that will shutdown the Scrapy process when it exceeds a memory limit, and also notify by email when that happened.

See Memory usage extension.

MEMUSAGE_LIMIT_MB

Default: 0

Scope: scrapy.extensions.memusage

The maximum amount of memory to allow (in megabytes) before shutting down Scrapy (if MEMUSAGE_ENABLED is True). If zero, no check will be performed.

See Memory usage extension.

MEMUSAGE_NOTIFY_MAIL

Default: False

Scope: scrapy.extensions.memusage

A list of emails to notify if the memory limit has been reached.

Example:

MEMUSAGE_NOTIFY_MAIL = ['user@example.com']

See Memory usage extension.

MEMUSAGE_REPORT

Default: False

Scope: scrapy.extensions.memusage

Whether to send a memory usage report after each spider has been closed.

See Memory usage extension.

MEMUSAGE_WARNING_MB

Default: 0

Scope: scrapy.extensions.memusage

The maximum amount of memory to allow (in megabytes) before sending a warning email notifying about it. If zero, no warning will be produced.

NEWSPIDER_MODULE

Default: ''

Module where to create new spiders using the genspider command.

Example:

NEWSPIDER_MODULE = 'mybot.spiders_dev'
RANDOMIZE_DOWNLOAD_DELAY

Default: True

If enabled, Scrapy will wait a random amount of time (between 0.5 and 1.5 * DOWNLOAD_DELAY) while fetching requests from the same website.

This randomization decreases the chance of the crawler being detected (and subsequently blocked) by sites which analyze requests looking for statistically significant similarities in the time between their requests.

The randomization policy is the same used by wget --random-wait option.

If DOWNLOAD_DELAY is zero (default) this option has no effect.

REACTOR_THREADPOOL_MAXSIZE

Default: 10

The maximum limit for Twisted Reactor thread pool size. This is common multi-purpose thread pool used by various Scrapy components. Threaded DNS Resolver, BlockingFeedStorage, S3FilesStore just to name a few. Increase this value if you’re experiencing problems with insufficient blocking IO.

REDIRECT_MAX_TIMES

Default: 20

Defines the maximum times a request can be redirected. After this maximum the request’s response is returned as is. We used Firefox default value for the same task.

REDIRECT_MAX_METAREFRESH_DELAY

Default: 100

Some sites use meta-refresh for redirecting to a session expired page, so we restrict automatic redirection to a maximum delay (in seconds)

REDIRECT_PRIORITY_ADJUST

Default: +2

Adjust redirect request priority relative to original request. A negative priority adjust means more priority.

ROBOTSTXT_OBEY

Default: False

Scope: scrapy.downloadermiddlewares.robotstxt

If enabled, Scrapy will respect robots.txt policies. For more information see RobotsTxtMiddleware

SCHEDULER

Default: 'scrapy.core.scheduler.Scheduler'

The scheduler to use for crawling.

SPIDER_CONTRACTS

Default:: {}

A dict containing the scrapy contracts enabled in your project, used for testing spiders. For more info see Spiders Contracts.

SPIDER_CONTRACTS_BASE

Default:

{
    'scrapy.contracts.default.UrlContract' : 1,
    'scrapy.contracts.default.ReturnsContract': 2,
    'scrapy.contracts.default.ScrapesContract': 3,
}

A dict containing the scrapy contracts enabled by default in Scrapy. You should never modify this setting in your project, modify SPIDER_CONTRACTS instead. For more info see Spiders Contracts.

SPIDER_LOADER_CLASS

Default: 'scrapy.spiderloader.SpiderLoader'

The class that will be used for loading spiders, which must implement the SpiderLoader API.

SPIDER_MIDDLEWARES

Default:: {}

A dict containing the spider middlewares enabled in your project, and their orders. For more info see Activating a spider middleware.

SPIDER_MIDDLEWARES_BASE

Default:

{
    'scrapy.spidermiddlewares.httperror.HttpErrorMiddleware': 50,
    'scrapy.spidermiddlewares.offsite.OffsiteMiddleware': 500,
    'scrapy.spidermiddlewares.referer.RefererMiddleware': 700,
    'scrapy.spidermiddlewares.urllength.UrlLengthMiddleware': 800,
    'scrapy.spidermiddlewares.depth.DepthMiddleware': 900,
}

A dict containing the spider middlewares enabled by default in Scrapy. You should never modify this setting in your project, modify SPIDER_MIDDLEWARES instead. For more info see Activating a spider middleware.

SPIDER_MODULES

Default: []

A list of modules where Scrapy will look for spiders.

Example:

SPIDER_MODULES = ['mybot.spiders_prod', 'mybot.spiders_dev']
STATS_CLASS

Default: 'scrapy.statscollectors.MemoryStatsCollector'

The class to use for collecting stats, who must implement the Stats Collector API.

STATS_DUMP

Default: True

Dump the Scrapy stats (to the Scrapy log) once the spider finishes.

For more info see: Stats Collection.

STATSMAILER_RCPTS

Default: [] (empty list)

Send Scrapy stats after spiders finish scraping. See StatsMailer for more info.

TELNETCONSOLE_ENABLED

Default: True

A boolean which specifies if the telnet console will be enabled (provided its extension is also enabled).

TELNETCONSOLE_PORT

Default: [6023, 6073]

The port range to use for the telnet console. If set to None or 0, a dynamically assigned port is used. For more info see Telnet Console.

TEMPLATES_DIR

Default: templates dir inside scrapy module

The directory where to look for templates when creating new projects with startproject command.

URLLENGTH_LIMIT

Default: 2083

Scope: spidermiddlewares.urllength

The maximum URL length to allow for crawled URLs. For more information about the default value for this setting see: http://www.boutell.com/newfaq/misc/urllength.html

USER_AGENT

Default: "Scrapy/VERSION (+http://scrapy.org)"

The default User-Agent to use when crawling, unless overridden.

Settings documented elsewhere:

The following settings are documented elsewhere, please check each specific case to see how to enable and use them.

Exceptions

Built-in Exceptions reference

Here’s a list of all exceptions included in Scrapy and their usage.

DropItem
exception scrapy.exceptions.DropItem

The exception that must be raised by item pipeline stages to stop processing an Item. For more information see Item Pipeline.

CloseSpider
exception scrapy.exceptions.CloseSpider(reason='cancelled')

This exception can be raised from a spider callback to request the spider to be closed/stopped. Supported arguments:

Parameters:reason (str) – the reason for closing

For example:

def parse_page(self, response):
    if 'Bandwidth exceeded' in response.body:
        raise CloseSpider('bandwidth_exceeded')
IgnoreRequest
exception scrapy.exceptions.IgnoreRequest

This exception can be raised by the Scheduler or any downloader middleware to indicate that the request should be ignored.

NotConfigured
exception scrapy.exceptions.NotConfigured

This exception can be raised by some components to indicate that they will remain disabled. Those components include:

  • Extensions
  • Item pipelines
  • Downloader middlewares
  • Spider middlewares

The exception must be raised in the component constructor.

NotSupported
exception scrapy.exceptions.NotSupported

This exception is raised to indicate an unsupported feature.

Command line tool
Learn about the command-line tool used to manage your Scrapy project.
Spiders
Write the rules to crawl your websites.
Selectors
Extract the data from web pages using XPath.
Scrapy shell
Test your extraction code in an interactive environment.
Items
Define the data you want to scrape.
Item Loaders
Populate your items with the extracted data.
Item Pipeline
Post-process and store your scraped data.
Feed exports
Output your scraped data using different formats and storages.
Requests and Responses
Understand the classes used to represent HTTP requests and responses.
Link Extractors
Convenient classes to extract links to follow from pages.
Settings
Learn how to configure Scrapy and see all available settings.
Exceptions
See all available exceptions and their meaning.

Built-in services

Logging

Note

scrapy.log has been deprecated alongside its functions in favor of explicit calls to the Python standard logging. Keep reading to learn more about the new logging system.

Scrapy uses Python’s builtin logging system for event logging. We’ll provide some simple examples to get you started, but for more advanced use-cases it’s strongly suggested to read thoroughly its documentation.

Logging works out of the box, and can be configured to some extent with the Scrapy settings listed in Logging settings.

Scrapy calls scrapy.utils.log.configure_logging() to set some reasonable defaults and handle those settings in Logging settings when running commands, so it’s recommended to manually call it if you’re running Scrapy from scripts as described in Run Scrapy from a script.

Log levels

Python’s builtin logging defines 5 different levels to indicate severity on a given log message. Here are the standard ones, listed in decreasing order:

  1. logging.CRITICAL - for critical errors (highest severity)
  2. logging.ERROR - for regular errors
  3. logging.WARNING - for warning messages
  4. logging.INFO - for informational messages
  5. logging.DEBUG - for debugging messages (lowest severity)

How to log messages

Here’s a quick example of how to log a message using the logging.WARNING level:

import logging
logging.warning("This is a warning")

There are shortcuts for issuing log messages on any of the standard 5 levels, and there’s also a general logging.log method which takes a given level as argument. If you need so, last example could be rewrote as:

import logging
logging.log(logging.WARNING, "This is a warning")

On top of that, you can create different “loggers” to encapsulate messages (For example, a common practice it’s to create different loggers for every module). These loggers can be configured independently, and they allow hierarchical constructions.

Last examples use the root logger behind the scenes, which is a top level logger where all messages are propagated to (unless otherwise specified). Using logging helpers is merely a shortcut for getting the root logger explicitly, so this is also an equivalent of last snippets:

import logging
logger = logging.getLogger()
logger.warning("This is a warning")

You can use a different logger just by getting its name with the logging.getLogger function:

import logging
logger = logging.getLogger('mycustomlogger')
logger.warning("This is a warning")

Finally, you can ensure having a custom logger for any module you’re working on by using the __name__ variable, which is populated with current module’s path:

import logging
logger = logging.getLogger(__name__)
logger.warning("This is a warning")

See also

Module logging, HowTo
Basic Logging Tutorial
Module logging, Loggers
Further documentation on loggers

Logging from Spiders

Scrapy provides a logger within each Spider instance, that can be accessed and used like this:

import scrapy

class MySpider(scrapy.Spider):

    name = 'myspider'
    start_urls = ['http://scrapinghub.com']

    def parse(self, response):
        self.logger.info('Parse function called on %s', response.url)

That logger is created using the Spider’s name, but you can use any custom Python logger you want. For example:

import logging
import scrapy

logger = logging.getLogger('mycustomlogger')

class MySpider(scrapy.Spider):

    name = 'myspider'
    start_urls = ['http://scrapinghub.com']

    def parse(self, response):
        logger.info('Parse function called on %s', response.url)

Logging configuration

Loggers on their own don’t manage how messages sent through them are displayed. For this task, different “handlers” can be attached to any logger instance and they will redirect those messages to appropriate destinations, such as the standard output, files, emails, etc.

By default, Scrapy sets and configures a handler for the root logger, based on the settings below.

Logging settings

These settings can be used to configure the logging:

First couple of settings define a destination for log messages. If LOG_FILE is set, messages sent through the root logger will be redirected to a file named LOG_FILE with encoding LOG_ENCODING. If unset and LOG_ENABLED is True, log messages will be displayed on the standard error. Lastly, if LOG_ENABLED is False, there won’t be any visible log output.

LOG_LEVEL determines the minimum level of severity to display, those messages with lower severity will be filtered out. It ranges through the possible levels listed in Log levels.

LOG_FORMAT and LOG_DATEFORMAT specify formatting strings used as layouts for all messages. Those strings can contain any placeholders listed in logging’s logrecord attributes docs and datetime’s strftime and strptime directives respectively.

Command-line options

There are command-line arguments, available for all commands, that you can use to override some of the Scrapy settings regarding logging.

See also

Module logging.handlers
Further documentation on available handlers

scrapy.utils.log module

scrapy.utils.log.configure_logging(settings=None, install_root_handler=True)

Initialize logging defaults for Scrapy.

Parameters:
  • settings (dict, Settings object or None) – settings used to create and configure a handler for the root logger (default: None).
  • install_root_handler (bool) – whether to install root logging handler (default: True)

This function does:

  • Route warnings and twisted logging through Python standard logging
  • Assign DEBUG and ERROR level to Scrapy and Twisted loggers respectively
  • Route stdout to log if LOG_STDOUT setting is True

When install_root_handler is True (default), this function also creates a handler for the root logger according to given settings (see Logging settings). You can override default options using settings argument. When settings is empty or None, defaults are used.

configure_logging is automatically called when using Scrapy commands, but needs to be called explicitly when running custom scripts. In that case, its usage is not required but it’s recommended.

If you plan on configuring the handlers yourself is still recommended you call this function, passing install_root_handler=False. Bear in mind there won’t be any log output set by default in that case.

To get you started on manually configuring logging’s output, you can use logging.basicConfig() to set a basic root handler. This is an example on how to redirect INFO or higher messages to a file:

import logging
from scrapy.utils.log import configure_logging

configure_logging(install_root_handler=False)
logging.basicConfig(
    filename='log.txt',
    format='%(levelname)s: %(message)s',
    level=logging.INFO
)

Refer to Run Scrapy from a script for more details about using Scrapy this way.

Stats Collection

Scrapy provides a convenient facility for collecting stats in the form of key/values, where values are often counters. The facility is called the Stats Collector, and can be accessed through the stats attribute of the Crawler API, as illustrated by the examples in the Common Stats Collector uses section below.

However, the Stats Collector is always available, so you can always import it in your module and use its API (to increment or set new stat keys), regardless of whether the stats collection is enabled or not. If it’s disabled, the API will still work but it won’t collect anything. This is aimed at simplifying the stats collector usage: you should spend no more than one line of code for collecting stats in your spider, Scrapy extension, or whatever code you’re using the Stats Collector from.

Another feature of the Stats Collector is that it’s very efficient (when enabled) and extremely efficient (almost unnoticeable) when disabled.

The Stats Collector keeps a stats table per open spider which is automatically opened when the spider is opened, and closed when the spider is closed.

Common Stats Collector uses

Access the stats collector through the stats attribute. Here is an example of an extension that access stats:

class ExtensionThatAccessStats(object):

    def __init__(self, stats):
        self.stats = stats

    @classmethod
    def from_crawler(cls, crawler):
        return cls(crawler.stats)

Set stat value:

stats.set_value('hostname', socket.gethostname())

Increment stat value:

stats.inc_value('pages_crawled')

Set stat value only if greater than previous:

stats.max_value('max_items_scraped', value)

Set stat value only if lower than previous:

stats.min_value('min_free_memory_percent', value)

Get stat value:

>>> stats.get_value('pages_crawled')
8

Get all stats:

>>> stats.get_stats()
{'pages_crawled': 1238, 'start_time': datetime.datetime(2009, 7, 14, 21, 47, 28, 977139)}

Available Stats Collectors

Besides the basic StatsCollector there are other Stats Collectors available in Scrapy which extend the basic Stats Collector. You can select which Stats Collector to use through the STATS_CLASS setting. The default Stats Collector used is the MemoryStatsCollector.

MemoryStatsCollector
class scrapy.statscollectors.MemoryStatsCollector

A simple stats collector that keeps the stats of the last scraping run (for each spider) in memory, after they’re closed. The stats can be accessed through the spider_stats attribute, which is a dict keyed by spider domain name.

This is the default Stats Collector used in Scrapy.

spider_stats

A dict of dicts (keyed by spider name) containing the stats of the last scraping run for each spider.

DummyStatsCollector
class scrapy.statscollectors.DummyStatsCollector

A Stats collector which does nothing but is very efficient (because it does nothing). This stats collector can be set via the STATS_CLASS setting, to disable stats collect in order to improve performance. However, the performance penalty of stats collection is usually marginal compared to other Scrapy workload like parsing pages.

Sending e-mail

Although Python makes sending e-mails relatively easy via the smtplib library, Scrapy provides its own facility for sending e-mails which is very easy to use and it’s implemented using Twisted non-blocking IO, to avoid interfering with the non-blocking IO of the crawler. It also provides a simple API for sending attachments and it’s very easy to configure, with a few settings.

Quick example

There are two ways to instantiate the mail sender. You can instantiate it using the standard constructor:

from scrapy.mail import MailSender
mailer = MailSender()

Or you can instantiate it passing a Scrapy settings object, which will respect the settings:

mailer = MailSender.from_settings(settings)

And here is how to use it to send an e-mail (without attachments):

mailer.send(to=["someone@example.com"], subject="Some subject", body="Some body", cc=["another@example.com"])

MailSender class reference

MailSender is the preferred class to use for sending emails from Scrapy, as it uses Twisted non-blocking IO, like the rest of the framework.

class scrapy.mail.MailSender(smtphost=None, mailfrom=None, smtpuser=None, smtppass=None, smtpport=None)
Parameters:
  • smtphost (str) – the SMTP host to use for sending the emails. If omitted, the MAIL_HOST setting will be used.
  • mailfrom (str) – the address used to send emails (in the From: header). If omitted, the MAIL_FROM setting will be used.
  • smtpuser – the SMTP user. If omitted, the MAIL_USER setting will be used. If not given, no SMTP authentication will be performed.
  • smtppass (str) – the SMTP pass for authentication.
  • smtpport (int) – the SMTP port to connect to
  • smtptls (boolean) – enforce using SMTP STARTTLS
  • smtpssl (boolean) – enforce using a secure SSL connection
classmethod from_settings(settings)

Instantiate using a Scrapy settings object, which will respect these Scrapy settings.

Parameters:settings (scrapy.settings.Settings object) – the e-mail recipients
send(to, subject, body, cc=None, attachs=(), mimetype='text/plain')

Send email to the given recipients.

Parameters:
  • to (list) – the e-mail recipients
  • subject (str) – the subject of the e-mail
  • cc (list) – the e-mails to CC
  • body (str) – the e-mail body
  • attachs (iterable) – an iterable of tuples (attach_name, mimetype, file_object) where attach_name is a string with the name that will appear on the e-mail’s attachment, mimetype is the mimetype of the attachment and file_object is a readable file object with the contents of the attachment
  • mimetype (str) – the MIME type of the e-mail

Mail settings

These settings define the default constructor values of the MailSender class, and can be used to configure e-mail notifications in your project without writing any code (for those extensions and code that uses MailSender).

MAIL_FROM

Default: 'scrapy@localhost'

Sender email to use (From: header) for sending emails.

MAIL_HOST

Default: 'localhost'

SMTP host to use for sending emails.

MAIL_PORT

Default: 25

SMTP port to use for sending emails.

MAIL_USER

Default: None

User to use for SMTP authentication. If disabled no SMTP authentication will be performed.

MAIL_PASS

Default: None

Password to use for SMTP authentication, along with MAIL_USER.

MAIL_TLS

Default: False

Enforce using STARTTLS. STARTTLS is a way to take an existing insecure connection, and upgrade it to a secure connection using SSL/TLS.

MAIL_SSL

Default: False

Enforce connecting using an SSL encrypted connection

Telnet Console

Scrapy comes with a built-in telnet console for inspecting and controlling a Scrapy running process. The telnet console is just a regular python shell running inside the Scrapy process, so you can do literally anything from it.

The telnet console is a built-in Scrapy extension which comes enabled by default, but you can also disable it if you want. For more information about the extension itself see Telnet console extension.

How to access the telnet console

The telnet console listens in the TCP port defined in the TELNETCONSOLE_PORT setting, which defaults to 6023. To access the console you need to type:

telnet localhost 6023
>>>

You need the telnet program which comes installed by default in Windows, and most Linux distros.

Available variables in the telnet console

The telnet console is like a regular Python shell running inside the Scrapy process, so you can do anything from it including importing new modules, etc.

However, the telnet console comes with some default variables defined for convenience:

Shortcut Description
crawler the Scrapy Crawler (scrapy.crawler.Crawler object)
engine Crawler.engine attribute
spider the active spider
slot the engine slot
extensions the Extension Manager (Crawler.extensions attribute)
stats the Stats Collector (Crawler.stats attribute)
settings the Scrapy settings object (Crawler.settings attribute)
est print a report of the engine status
prefs for memory debugging (see Debugging memory leaks)
p a shortcut to the pprint.pprint function
hpy for memory debugging (see Debugging memory leaks)

Telnet console usage examples

Here are some example tasks you can do with the telnet console:

View engine status

You can use the est() method of the Scrapy engine to quickly show its state using the telnet console:

telnet localhost 6023
>>> est()
Execution engine status

time()-engine.start_time                        : 8.62972998619
engine.has_capacity()                           : False
len(engine.downloader.active)                   : 16
engine.scraper.is_idle()                        : False
engine.spider.name                              : followall
engine.spider_is_idle(engine.spider)            : False
engine.slot.closing                             : False
len(engine.slot.inprogress)                     : 16
len(engine.slot.scheduler.dqs or [])            : 0
len(engine.slot.scheduler.mqs)                  : 92
len(engine.scraper.slot.queue)                  : 0
len(engine.scraper.slot.active)                 : 0
engine.scraper.slot.active_size                 : 0
engine.scraper.slot.itemproc_size               : 0
engine.scraper.slot.needs_backout()             : False
Pause, resume and stop the Scrapy engine

To pause:

telnet localhost 6023
>>> engine.pause()
>>>

To resume:

telnet localhost 6023
>>> engine.unpause()
>>>

To stop:

telnet localhost 6023
>>> engine.stop()
Connection closed by foreign host.

Telnet Console signals

scrapy.telnet.update_telnet_vars(telnet_vars)

Sent just before the telnet console is opened. You can hook up to this signal to add, remove or update the variables that will be available in the telnet local namespace. In order to do that, you need to update the telnet_vars dict in your handler.

Parameters:telnet_vars (dict) – the dict of telnet variables

Telnet settings

These are the settings that control the telnet console’s behaviour:

TELNETCONSOLE_PORT

Default: [6023, 6073]

The port range to use for the telnet console. If set to None or 0, a dynamically assigned port is used.

TELNETCONSOLE_HOST

Default: '127.0.0.1'

The interface the telnet console should listen on

Web Service

webservice has been moved into a separate project.

It is hosted at:

Logging
Learn how to use Python’s builtin logging on Scrapy.
Stats Collection
Collect statistics about your scraping crawler.
Sending e-mail
Send email notifications when certain events occur.
Telnet Console
Inspect a running crawler using a built-in Python console.
Web Service
Monitor and control a crawler using a web service.

Solving specific problems

Frequently Asked Questions

How does Scrapy compare to BeautifulSoup or lxml?

BeautifulSoup and lxml are libraries for parsing HTML and XML. Scrapy is an application framework for writing web spiders that crawl web sites and extract data from them.

Scrapy provides a built-in mechanism for extracting data (called selectors) but you can easily use BeautifulSoup (or lxml) instead, if you feel more comfortable working with them. After all, they’re just parsing libraries which can be imported and used from any Python code.

In other words, comparing BeautifulSoup (or lxml) to Scrapy is like comparing jinja2 to Django.

What Python versions does Scrapy support?

Scrapy is supported under Python 2.7 only. Python 2.6 support was dropped starting at Scrapy 0.20.

Does Scrapy work with Python 3?

No, but there are plans to support Python 3.3+. At the moment, Scrapy works with Python 2.7.

See also

What Python versions does Scrapy support?.

Did Scrapy “steal” X from Django?

Probably, but we don’t like that word. We think Django is a great open source project and an example to follow, so we’ve used it as an inspiration for Scrapy.

We believe that, if something is already done well, there’s no need to reinvent it. This concept, besides being one of the foundations for open source and free software, not only applies to software but also to documentation, procedures, policies, etc. So, instead of going through each problem ourselves, we choose to copy ideas from those projects that have already solved them properly, and focus on the real problems we need to solve.

We’d be proud if Scrapy serves as an inspiration for other projects. Feel free to steal from us!

Does Scrapy work with HTTP proxies?

Yes. Support for HTTP proxies is provided (since Scrapy 0.8) through the HTTP Proxy downloader middleware. See HttpProxyMiddleware.

How can I scrape an item with attributes in different pages?

See Passing additional data to callback functions.

Scrapy crashes with: ImportError: No module named win32api

You need to install pywin32 because of this Twisted bug.

How can I simulate a user login in my spider?

See Using FormRequest.from_response() to simulate a user login.

Does Scrapy crawl in breadth-first or depth-first order?

By default, Scrapy uses a LIFO queue for storing pending requests, which basically means that it crawls in DFO order. This order is more convenient in most cases. If you do want to crawl in true BFO order, you can do it by setting the following settings:

DEPTH_PRIORITY = 1
SCHEDULER_DISK_QUEUE = 'scrapy.squeues.PickleFifoDiskQueue'
SCHEDULER_MEMORY_QUEUE = 'scrapy.squeues.FifoMemoryQueue'

My Scrapy crawler has memory leaks. What can I do?

See Debugging memory leaks.

Also, Python has a builtin memory leak issue which is described in Leaks without leaks.

How can I make Scrapy consume less memory?

See previous question.

Can I use Basic HTTP Authentication in my spiders?

Yes, see HttpAuthMiddleware.

Why does Scrapy download pages in English instead of my native language?

Try changing the default Accept-Language request header by overriding the DEFAULT_REQUEST_HEADERS setting.

Where can I find some example Scrapy projects?

See Examples.

Can I run a spider without creating a project?

Yes. You can use the runspider command. For example, if you have a spider written in a my_spider.py file you can run it with:

scrapy runspider my_spider.py

See runspider command for more info.

I get “Filtered offsite request” messages. How can I fix them?

Those messages (logged with DEBUG level) don’t necessarily mean there is a problem, so you may not need to fix them.

Those messages are thrown by the Offsite Spider Middleware, which is a spider middleware (enabled by default) whose purpose is to filter out requests to domains outside the ones covered by the spider.

For more info see: OffsiteMiddleware.

Can I use JSON for large exports?

It’ll depend on how large your output is. See this warning in JsonItemExporter documentation.

Can I return (Twisted) deferreds from signal handlers?

Some signals support returning deferreds from their handlers, others don’t. See the Built-in signals reference to know which ones.

What does the response status code 999 means?

999 is a custom response status code used by Yahoo sites to throttle requests. Try slowing down the crawling speed by using a download delay of 2 (or higher) in your spider:

class MySpider(CrawlSpider):

    name = 'myspider'

    download_delay = 2

    # [ ... rest of the spider code ... ]

Or by setting a global download delay in your project with the DOWNLOAD_DELAY setting.

Can I call pdb.set_trace() from my spiders to debug them?

Yes, but you can also use the Scrapy shell which allows you to quickly analyze (and even modify) the response being processed by your spider, which is, quite often, more useful than plain old pdb.set_trace().

For more info see Invoking the shell from spiders to inspect responses.

Simplest way to dump all my scraped items into a JSON/CSV/XML file?

To dump into a JSON file:

scrapy crawl myspider -o items.json

To dump into a CSV file:

scrapy crawl myspider -o items.csv

To dump into a XML file:

scrapy crawl myspider -o items.xml

For more information see Feed exports

What’s this huge cryptic __VIEWSTATE parameter used in some forms?

The __VIEWSTATE parameter is used in sites built with ASP.NET/VB.NET. For more info on how it works see this page. Also, here’s an example spider which scrapes one of these sites.

What’s the best way to parse big XML/CSV data feeds?

Parsing big feeds with XPath selectors can be problematic since they need to build the DOM of the entire feed in memory, and this can be quite slow and consume a lot of memory.

In order to avoid parsing all the entire feed at once in memory, you can use the functions xmliter and csviter from scrapy.utils.iterators module. In fact, this is what the feed spiders (see Spiders) use under the cover.

Does Scrapy manage cookies automatically?

Yes, Scrapy receives and keeps track of cookies sent by servers, and sends them back on subsequent requests, like any regular web browser does.

For more info see Requests and Responses and CookiesMiddleware.

How can I see the cookies being sent and received from Scrapy?

Enable the COOKIES_DEBUG setting.

How can I instruct a spider to stop itself?

Raise the CloseSpider exception from a callback. For more info see: CloseSpider.

How can I prevent my Scrapy bot from getting banned?

See Avoiding getting banned.

Should I use spider arguments or settings to configure my spider?

Both spider arguments and settings can be used to configure your spider. There is no strict rule that mandates to use one or the other, but settings are more suited for parameters that, once set, don’t change much, while spider arguments are meant to change more often, even on each spider run and sometimes are required for the spider to run at all (for example, to set the start url of a spider).

To illustrate with an example, assuming you have a spider that needs to log into a site to scrape data, and you only want to scrape data from a certain section of the site (which varies each time). In that case, the credentials to log in would be settings, while the url of the section to scrape would be a spider argument.

I’m scraping a XML document and my XPath selector doesn’t return any items

You may need to remove namespaces. See Removing namespaces.

Debugging Spiders

This document explains the most common techniques for debugging spiders. Consider the following scrapy spider below:

import scrapy
from myproject.items import MyItem

class MySpider(scrapy.Spider):
    name = 'myspider'
    start_urls = (
        'http://example.com/page1',
        'http://example.com/page2',
        )

    def parse(self, response):
        # collect `item_urls`
        for item_url in item_urls:
            yield scrapy.Request(item_url, self.parse_item)

    def parse_item(self, response):
        item = MyItem()
        # populate `item` fields
        # and extract item_details_url
        yield scrapy.Request(item_details_url, self.parse_details, meta={'item': item})

    def parse_details(self, response):
        item = response.meta['item']
        # populate more `item` fields
        return item

Basically this is a simple spider which parses two pages of items (the start_urls). Items also have a details page with additional information, so we use the meta functionality of Request to pass a partially populated item.

Parse Command

The most basic way of checking the output of your spider is to use the parse command. It allows to check the behaviour of different parts of the spider at the method level. It has the advantage of being flexible and simple to use, but does not allow debugging code inside a method.

In order to see the item scraped from a specific url:

$ scrapy parse --spider=myspider -c parse_item -d 2 <item_url>
[ ... scrapy log lines crawling example.com spider ... ]

>>> STATUS DEPTH LEVEL 2 <<<
# Scraped Items  ------------------------------------------------------------
[{'url': <item_url>}]

# Requests  -----------------------------------------------------------------
[]

Using the --verbose or -v option we can see the status at each depth level:

$ scrapy parse --spider=myspider -c parse_item -d 2 -v <item_url>
[ ... scrapy log lines crawling example.com spider ... ]

>>> DEPTH LEVEL: 1 <<<
# Scraped Items  ------------------------------------------------------------
[]

# Requests  -----------------------------------------------------------------
[<GET item_details_url>]


>>> DEPTH LEVEL: 2 <<<
# Scraped Items  ------------------------------------------------------------
[{'url': <item_url>}]

# Requests  -----------------------------------------------------------------
[]

Checking items scraped from a single start_url, can also be easily achieved using:

$ scrapy parse --spider=myspider -d 3 'http://example.com/page1'

Scrapy Shell

While the parse command is very useful for checking behaviour of a spider, it is of little help to check what happens inside a callback, besides showing the response received and the output. How to debug the situation when parse_details sometimes receives no item?

Fortunately, the shell is your bread and butter in this case (see Invoking the shell from spiders to inspect responses):

from scrapy.shell import inspect_response

def parse_details(self, response):
    item = response.meta.get('item', None)
    if item:
        # populate more `item` fields
        return item
    else:
        inspect_response(response, self)

See also: Invoking the shell from spiders to inspect responses.

Open in browser

Sometimes you just want to see how a certain response looks in a browser, you can use the open_in_browser function for that. Here is an example of how you would use it:

from scrapy.utils.response import open_in_browser

def parse_details(self, response):
    if "item name" not in response.body:
        open_in_browser(response)

open_in_browser will open a browser with the response received by Scrapy at that point, adjusting the base tag so that images and styles are displayed properly.

Logging

Logging is another useful option for getting information about your spider run. Although not as convenient, it comes with the advantage that the logs will be available in all future runs should they be necessary again:

def parse_details(self, response):
    item = response.meta.get('item', None)
    if item:
        # populate more `item` fields
        return item
    else:
        self.logger.warning('No item received for %s', response.url)

For more information, check the Logging section.

Spiders Contracts

New in version 0.15.

Note

This is a new feature (introduced in Scrapy 0.15) and may be subject to minor functionality/API updates. Check the release notes to be notified of updates.

Testing spiders can get particularly annoying and while nothing prevents you from writing unit tests the task gets cumbersome quickly. Scrapy offers an integrated way of testing your spiders by the means of contracts.

This allows you to test each callback of your spider by hardcoding a sample url and check various constraints for how the callback processes the response. Each contract is prefixed with an @ and included in the docstring. See the following example:

def parse(self, response):
    """ This function parses a sample response. Some contracts are mingled
    with this docstring.

    @url http://www.amazon.com/s?field-keywords=selfish+gene
    @returns items 1 16
    @returns requests 0 0
    @scrapes Title Author Year Price
    """

This callback is tested using three built-in contracts:

class scrapy.contracts.default.UrlContract

This contract (@url) sets the sample url used when checking other contract conditions for this spider. This contract is mandatory. All callbacks lacking this contract are ignored when running the checks:

@url url
class scrapy.contracts.default.ReturnsContract

This contract (@returns) sets lower and upper bounds for the items and requests returned by the spider. The upper bound is optional:

@returns item(s)|request(s) [min [max]]
class scrapy.contracts.default.ScrapesContract

This contract (@scrapes) checks that all the items returned by the callback have the specified fields:

@scrapes field_1 field_2 ...

Use the check command to run the contract checks.

Custom Contracts

If you find you need more power than the built-in scrapy contracts you can create and load your own contracts in the project by using the SPIDER_CONTRACTS setting:

SPIDER_CONTRACTS = {
    'myproject.contracts.ResponseCheck': 10,
    'myproject.contracts.ItemValidate': 10,
}

Each contract must inherit from scrapy.contracts.Contract and can override three methods:

class scrapy.contracts.Contract(method, *args)
Parameters:
  • method (function) – callback function to which the contract is associated
  • args (list) – list of arguments passed into the docstring (whitespace separated)
adjust_request_args(args)

This receives a dict as an argument containing default arguments for Request object. Must return the same or a modified version of it.

pre_process(response)

This allows hooking in various checks on the response received from the sample request, before it’s being passed to the callback.

post_process(output)

This allows processing the output of the callback. Iterators are converted listified before being passed to this hook.

Here is a demo contract which checks the presence of a custom header in the response received. Raise scrapy.exceptions.ContractFail in order to get the failures pretty printed:

from scrapy.contracts import Contract
from scrapy.exceptions import ContractFail

class HasHeaderContract(Contract):
    """ Demo contract which checks the presence of a custom header
        @has_header X-CustomHeader
    """

    name = 'has_header'

    def pre_process(self, response):
        for header in self.args:
            if header not in response.headers:
                raise ContractFail('X-CustomHeader not present')

Common Practices

This section documents common practices when using Scrapy. These are things that cover many topics and don’t often fall into any other specific section.

Run Scrapy from a script

You can use the API to run Scrapy from a script, instead of the typical way of running Scrapy via scrapy crawl.

Remember that Scrapy is built on top of the Twisted asynchronous networking library, so you need to run it inside the Twisted reactor.

First utility you can use to run your spiders is scrapy.crawler.CrawlerProcess. This class will start a Twisted reactor for you, configuring the logging and setting shutdown handlers. This class is the one used by all Scrapy commands.

Here’s an example showing how to run a single spider with it.

import scrapy
from scrapy.crawler import CrawlerProcess

class MySpider(scrapy.Spider):
    # Your spider definition
    ...

process = CrawlerProcess({
    'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'
})

process.crawl(MySpider)
process.start() # the script will block here until the crawling is finished

Make sure to check CrawlerProcess documentation to get acquainted with its usage details.

If you are inside a Scrapy project there are some additional helpers you can use to import those components within the project. You can automatically import your spiders passing their name to CrawlerProcess, and use get_project_settings to get a Settings instance with your project settings.

What follows is a working example of how to do that, using the testspiders project as example.

from scrapy.crawler import CrawlerProcess
from scrapy.utils.project import get_project_settings

process = CrawlerProcess(get_project_settings())

# 'followall' is the name of one of the spiders of the project.
process.crawl('followall', domain='scrapinghub.com')
process.start() # the script will block here until the crawling is finished

There’s another Scrapy utility that provides more control over the crawling process: scrapy.crawler.CrawlerRunner. This class is a thin wrapper that encapsulates some simple helpers to run multiple crawlers, but it won’t start or interfere with existing reactors in any way.

Using this class the reactor should be explicitly run after scheduling your spiders. It’s recommended you use CrawlerRunner instead of CrawlerProcess if your application is already using Twisted and you want to run Scrapy in the same reactor.

Note that you will also have to shutdown the Twisted reactor yourself after the spider is finished. This can be achieved by adding callbacks to the deferred returned by the CrawlerRunner.crawl method.

Here’s an example of its usage, along with a callback to manually stop the reactor after MySpider has finished running.

from twisted.internet import reactor
import scrapy
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging

class MySpider(scrapy.Spider):
    # Your spider definition
    ...

configure_logging({'LOG_FORMAT': '%(levelname)s: %(message)s'})
runner = CrawlerRunner()

d = runner.crawl(MySpider)
d.addBoth(lambda _: reactor.stop())
reactor.run() # the script will block here until the crawling is finished

See also

Twisted Reactor Overview.

Running multiple spiders in the same process

By default, Scrapy runs a single spider per process when you run scrapy crawl. However, Scrapy supports running multiple spiders per process using the internal API.

Here is an example that runs multiple spiders simultaneously:

import scrapy
from scrapy.crawler import CrawlerProcess

class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...

class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...

process = CrawlerProcess()
process.crawl(MySpider1)
process.crawl(MySpider2)
process.start() # the script will block here until all crawling jobs are finished

Same example using CrawlerRunner:

import scrapy
from twisted.internet import reactor
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging

class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...

class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...

configure_logging()
runner = CrawlerRunner()
runner.crawl(MySpider1)
runner.crawl(MySpider2)
d = runner.join()
d.addBoth(lambda _: reactor.stop())

reactor.run() # the script will block here until all crawling jobs are finished

Same example but running the spiders sequentially by chaining the deferreds:

from twisted.internet import reactor, defer
from scrapy.crawler import CrawlerRunner
from scrapy.utils.log import configure_logging

class MySpider1(scrapy.Spider):
    # Your first spider definition
    ...

class MySpider2(scrapy.Spider):
    # Your second spider definition
    ...

configure_logging()
runner = CrawlerRunner()

@defer.inlineCallbacks
def crawl():
    yield runner.crawl(MySpider1)
    yield runner.crawl(MySpider2)
    reactor.stop()

crawl()
reactor.run() # the script will block here until the last crawl call is finished

See also

Run Scrapy from a script.

Distributed crawls

Scrapy doesn’t provide any built-in facility for running crawls in a distribute (multi-server) manner. However, there are some ways to distribute crawls, which vary depending on how you plan to distribute them.

If you have many spiders, the obvious way to distribute the load is to setup many Scrapyd instances and distribute spider runs among those.

If you instead want to run a single (big) spider through many machines, what you usually do is partition the urls to crawl and send them to each separate spider. Here is a concrete example:

First, you prepare the list of urls to crawl and put them into separate files/urls:

http://somedomain.com/urls-to-crawl/spider1/part1.list
http://somedomain.com/urls-to-crawl/spider1/part2.list
http://somedomain.com/urls-to-crawl/spider1/part3.list

Then you fire a spider run on 3 different Scrapyd servers. The spider would receive a (spider) argument part with the number of the partition to crawl:

curl http://scrapy1.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=1
curl http://scrapy2.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=2
curl http://scrapy3.mycompany.com:6800/schedule.json -d project=myproject -d spider=spider1 -d part=3

Avoiding getting banned

Some websites implement certain measures to prevent bots from crawling them, with varying degrees of sophistication. Getting around those measures can be difficult and tricky, and may sometimes require special infrastructure. Please consider contacting commercial support if in doubt.

Here are some tips to keep in mind when dealing with these kinds of sites:

  • rotate your user agent from a pool of well-known ones from browsers (google around to get a list of them)
  • disable cookies (see COOKIES_ENABLED) as some sites may use cookies to spot bot behaviour
  • use download delays (2 or higher). See DOWNLOAD_DELAY setting.
  • if possible, use Google cache to fetch pages, instead of hitting the sites directly
  • use a pool of rotating IPs. For example, the free Tor project or paid services like ProxyMesh
  • use a highly distributed downloader that circumvents bans internally, so you can just focus on parsing clean pages. One example of such downloaders is Crawlera

If you are still unable to prevent your bot getting banned, consider contacting commercial support.

Broad Crawls

Scrapy defaults are optimized for crawling specific sites. These sites are often handled by a single Scrapy spider, although this is not necessary or required (for example, there are generic spiders that handle any given site thrown at them).

In addition to this “focused crawl”, there is another common type of crawling which covers a large (potentially unlimited) number of domains, and is only limited by time or other arbitrary constraint, rather than stopping when the domain was crawled to completion or when there are no more requests to perform. These are called “broad crawls” and is the typical crawlers employed by search engines.

These are some common properties often found in broad crawls:

  • they crawl many domains (often, unbounded) instead of a specific set of sites
  • they don’t necessarily crawl domains to completion, because it would impractical (or impossible) to do so, and instead limit the crawl by time or number of pages crawled
  • they are simpler in logic (as opposed to very complex spiders with many extraction rules) because data is often post-processed in a separate stage
  • they crawl many domains concurrently, which allows them to achieve faster crawl speeds by not being limited by any particular site constraint (each site is crawled slowly to respect politeness, but many sites are crawled in parallel)

As said above, Scrapy default settings are optimized for focused crawls, not broad crawls. However, due to its asynchronous architecture, Scrapy is very well suited for performing fast broad crawls. This page summarizes some things you need to keep in mind when using Scrapy for doing broad crawls, along with concrete suggestions of Scrapy settings to tune in order to achieve an efficient broad crawl.

Increase concurrency

Concurrency is the number of requests that are processed in parallel. There is a global limit and a per-domain limit.

The default global concurrency limit in Scrapy is not suitable for crawling many different domains in parallel, so you will want to increase it. How much to increase it will depend on how much CPU you crawler will have available. A good starting point is 100, but the best way to find out is by doing some trials and identifying at what concurrency your Scrapy process gets CPU bounded. For optimum performance, You should pick a concurrency where CPU usage is at 80-90%.

To increase the global concurrency use:

CONCURRENT_REQUESTS = 100

Increase Twisted IO thread pool maximum size

Currently Scrapy does DNS resolution in a blocking way with usage of thread pool. With higher concurrency levels the crawling could be slow or even fail hitting DNS resolver timeouts. Possible solution to increase the number of threads handling DNS queries. The DNS queue will be processed faster speeding up establishing of connection and crawling overall.

To increase maximum thread pool size use:

REACTOR_THREADPOOL_MAXSIZE = 20

Setup your own DNS

If you have multiple crawling processes and single central DNS, it can act like DoS attack on the DNS server resulting to slow down of entire network or even blocking your machines. To avoid this setup your own DNS server with local cache and upstream to some large DNS like OpenDNS or Verizon.

Reduce log level

When doing broad crawls you are often only interested in the crawl rates you get and any errors found. These stats are reported by Scrapy when using the INFO log level. In order to save CPU (and log storage requirements) you should not use DEBUG log level when preforming large broad crawls in production. Using DEBUG level when developing your (broad) crawler may fine though.

To set the log level use:

LOG_LEVEL = 'INFO'

Disable cookies

Disable cookies unless you really need. Cookies are often not needed when doing broad crawls (search engine crawlers ignore them), and they improve performance by saving some CPU cycles and reducing the memory footprint of your Scrapy crawler.

To disable cookies use:

COOKIES_ENABLED = False

Disable retries

Retrying failed HTTP requests can slow down the crawls substantially, specially when sites causes are very slow (or fail) to respond, thus causing a timeout error which gets retried many times, unnecessarily, preventing crawler capacity to be reused for other domains.

To disable retries use:

RETRY_ENABLED = False

Reduce download timeout

Unless you are crawling from a very slow connection (which shouldn’t be the case for broad crawls) reduce the download timeout so that stuck requests are discarded quickly and free up capacity to process the next ones.

To reduce the download timeout use:

DOWNLOAD_TIMEOUT = 15

Disable redirects

Consider disabling redirects, unless you are interested in following them. When doing broad crawls it’s common to save redirects and resolve them when revisiting the site at a later crawl. This also help to keep the number of request constant per crawl batch, otherwise redirect loops may cause the crawler to dedicate too many resources on any specific domain.

To disable redirects use:

REDIRECT_ENABLED = False

Enable crawling of “Ajax Crawlable Pages”

Some pages (up to 1%, based on empirical data from year 2013) declare themselves as ajax crawlable. This means they provide plain HTML version of content that is usually available only via AJAX. Pages can indicate it in two ways:

  1. by using #! in URL - this is the default way;
  2. by using a special meta tag - this way is used on “main”, “index” website pages.

Scrapy handles (1) automatically; to handle (2) enable AjaxCrawlMiddleware:

AJAXCRAWL_ENABLED = True

When doing broad crawls it’s common to crawl a lot of “index” web pages; AjaxCrawlMiddleware helps to crawl them correctly. It is turned OFF by default because it has some performance overhead, and enabling it for focused crawls doesn’t make much sense.

Using Firefox for scraping

Here is a list of tips and advice on using Firefox for scraping, along with a list of useful Firefox add-ons to ease the scraping process.

Caveats with inspecting the live browser DOM

Since Firefox add-ons operate on a live browser DOM, what you’ll actually see when inspecting the page source is not the original HTML, but a modified one after applying some browser clean up and executing Javascript code. Firefox, in particular, is known for adding <tbody> elements to tables. Scrapy, on the other hand, does not modify the original page HTML, so you won’t be able to extract any data if you use <tbody in your XPath expressions.

Therefore, you should keep in mind the following things when working with Firefox and XPath:

  • Disable Firefox Javascript while inspecting the DOM looking for XPaths to be used in Scrapy
  • Never use full XPath paths, use relative and clever ones based on attributes (such as id, class, width, etc) or any identifying features like contains(@href, 'image').
  • Never include <tbody> elements in your XPath expressions unless you really know what you’re doing

Useful Firefox add-ons for scraping

Firebug

Firebug is a widely known tool among web developers and it’s also very useful for scraping. In particular, its Inspect Element feature comes very handy when you need to construct the XPaths for extracting data because it allows you to view the HTML code of each page element while moving your mouse over it.

See Using Firebug for scraping for a detailed guide on how to use Firebug with Scrapy.

XPather

XPather allows you to test XPath expressions directly on the pages.

XPath Checker

XPath Checker is another Firefox add-on for testing XPaths on your pages.

Tamper Data

Tamper Data is a Firefox add-on which allows you to view and modify the HTTP request headers sent by Firefox. Firebug also allows to view HTTP headers, but not to modify them.

Firecookie

Firecookie makes it easier to view and manage cookies. You can use this extension to create a new cookie, delete existing cookies, see a list of cookies for the current site, manage cookies permissions and a lot more.

Using Firebug for scraping

Note

Google Directory, the example website used in this guide is no longer available as it has been shut down by Google. The concepts in this guide are still valid though. If you want to update this guide to use a new (working) site, your contribution will be more than welcome!. See Contributing to Scrapy for information on how to do so.

Introduction

This document explains how to use Firebug (a Firefox add-on) to make the scraping process easier and more fun. For other useful Firefox add-ons see Useful Firefox add-ons for scraping. There are some caveats with using Firefox add-ons to inspect pages, see Caveats with inspecting the live browser DOM.

In this example, we’ll show how to use Firebug to scrape data from the Google Directory, which contains the same data as the Open Directory Project used in the tutorial but with a different face.

Firebug comes with a very useful feature called Inspect Element which allows you to inspect the HTML code of the different page elements just by hovering your mouse over them. Otherwise you would have to search for the tags manually through the HTML body which can be a very tedious task.

In the following screenshot you can see the Inspect Element tool in action.

Inspecting elements with Firebug

At first sight, we can see that the directory is divided in categories, which are also divided in subcategories.

However, it seems that there are more subcategories than the ones being shown in this page, so we’ll keep looking:

Inspecting elements with Firebug

As expected, the subcategories contain links to other subcategories, and also links to actual websites, which is the purpose of the directory.

Extracting the data

Now we’re going to write the code to extract data from those pages.

With the help of Firebug, we’ll take a look at some page containing links to websites (say http://directory.google.com/Top/Arts/Awards/) and find out how we can extract those links using Selectors. We’ll also use the Scrapy shell to test those XPath’s and make sure they work as we expect.

Inspecting elements with Firebug

As you can see, the page markup is not very descriptive: the elements don’t contain id, class or any attribute that clearly identifies them, so we’ll use the ranking bars as a reference point to select the data to extract when we construct our XPaths.

After using FireBug, we can see that each link is inside a td tag, which is itself inside a tr tag that also contains the link’s ranking bar (in another td).

So we can select the ranking bar, then find its parent (the tr), and then finally, the link’s td (which contains the data we want to scrape).

This results in the following XPath:

//td[descendant::a[contains(@href, "#pagerank")]]/following-sibling::td//a

It’s important to use the Scrapy shell to test these complex XPath expressions and make sure they work as expected.

Basically, that expression will look for the ranking bar’s td element, and then select any td element who has a descendant a element whose href attribute contains the string #pagerank“

Of course, this is not the only XPath, and maybe not the simpler one to select that data. Another approach could be, for example, to find any font tags that have that grey colour of the links,

Finally, we can write our parse_category() method:

def parse_category(self, response):
    # The path to website links in directory page
    links = response.xpath('//td[descendant::a[contains(@href, "#pagerank")]]/following-sibling::td/font')

    for link in links:
        item = DirectoryItem()
        item['name'] = link.xpath('a/text()').extract()
        item['url'] = link.xpath('a/@href').extract()
        item['description'] = link.xpath('font[2]/text()').extract()
        yield item

Be aware that you may find some elements which appear in Firebug but not in the original HTML, such as the typical case of <tbody> elements.

or tags which Therefer in page HTML sources may on Firebug inspects the live DOM

Debugging memory leaks

In Scrapy, objects such as Requests, Responses and Items have a finite lifetime: they are created, used for a while, and finally destroyed.

From all those objects, the Request is probably the one with the longest lifetime, as it stays waiting in the Scheduler queue until it’s time to process it. For more info see Architecture overview.

As these Scrapy objects have a (rather long) lifetime, there is always the risk of accumulating them in memory without releasing them properly and thus causing what is known as a “memory leak”.

To help debugging memory leaks, Scrapy provides a built-in mechanism for tracking objects references called trackref, and you can also use a third-party library called Guppy for more advanced memory debugging (see below for more info). Both mechanisms must be used from the Telnet Console.

Common causes of memory leaks

It happens quite often (sometimes by accident, sometimes on purpose) that the Scrapy developer passes objects referenced in Requests (for example, using the meta attribute or the request callback function) and that effectively bounds the lifetime of those referenced objects to the lifetime of the Request. This is, by far, the most common cause of memory leaks in Scrapy projects, and a quite difficult one to debug for newcomers.

In big projects, the spiders are typically written by different people and some of those spiders could be “leaking” and thus affecting the rest of the other (well-written) spiders when they get to run concurrently, which, in turn, affects the whole crawling process.

The leak could also come from a custom middleware, pipeline or extension that you have written, if you are not releasing the (previously allocated) resources properly. For example, allocating resources on spider_opened but not releasing them on spider_closed may cause problems if you’re running multiple spiders per process.

Too Many Requests?

By default Scrapy keeps the request queue in memory; it includes Request objects and all objects referenced in Request attributes (e.g. in meta). While not necessarily a leak, this can take a lot of memory. Enabling persistent job queue could help keeping memory usage in control.

Debugging memory leaks with trackref

trackref is a module provided by Scrapy to debug the most common cases of memory leaks. It basically tracks the references to all live Requests, Responses, Item and Selector objects.

You can enter the telnet console and inspect how many objects (of the classes mentioned above) are currently alive using the prefs() function which is an alias to the print_live_refs() function:

telnet localhost 6023

>>> prefs()
Live References

ExampleSpider                       1   oldest: 15s ago
HtmlResponse                       10   oldest: 1s ago
Selector                            2   oldest: 0s ago
FormRequest                       878   oldest: 7s ago

As you can see, that report also shows the “age” of the oldest object in each class. If you’re running multiple spiders per process chances are you can figure out which spider is leaking by looking at the oldest request or response. You can get the oldest object of each class using the get_oldest() function (from the telnet console).

Which objects are tracked?

The objects tracked by trackrefs are all from these classes (and all its subclasses):

A real example

Let’s see a concrete example of a hypothetical case of memory leaks. Suppose we have some spider with a line similar to this one:

return Request("http://www.somenastyspider.com/product.php?pid=%d" % product_id,
    callback=self.parse, meta={referer: response})

That line is passing a response reference inside a request which effectively ties the response lifetime to the requests’ one, and that would definitely cause memory leaks.

Let’s see how we can discover the cause (without knowing it a-priori, of course) by using the trackref tool.

After the crawler is running for a few minutes and we notice its memory usage has grown a lot, we can enter its telnet console and check the live references:

>>> prefs()
Live References

SomenastySpider                     1   oldest: 15s ago
HtmlResponse                     3890   oldest: 265s ago
Selector                            2   oldest: 0s ago
Request                          3878   oldest: 250s ago

The fact that there are so many live responses (and that they’re so old) is definitely suspicious, as responses should have a relatively short lifetime compared to Requests. The number of responses is similar to the number of requests, so it looks like they are tied in a some way. We can now go and check the code of the spider to discover the nasty line that is generating the leaks (passing response references inside requests).

Sometimes extra information about live objects can be helpful. Let’s check the oldest response:

>>> from scrapy.utils.trackref import get_oldest
>>> r = get_oldest('HtmlResponse')
>>> r.url
'http://www.somenastyspider.com/product.php?pid=123'

If you want to iterate over all objects, instead of getting the oldest one, you can use the scrapy.utils.trackref.iter_all() function:

>>> from scrapy.utils.trackref import iter_all
>>> [r.url for r in iter_all('HtmlResponse')]
['http://www.somenastyspider.com/product.php?pid=123',
 'http://www.somenastyspider.com/product.php?pid=584',
...
Too many spiders?

If your project has too many spiders executed in parallel, the output of prefs() can be difficult to read. For this reason, that function has a ignore argument which can be used to ignore a particular class (and all its subclases). For example, this won’t show any live references to spiders:

>>> from scrapy.spiders import Spider
>>> prefs(ignore=Spider)
scrapy.utils.trackref module

Here are the functions available in the trackref module.

class scrapy.utils.trackref.object_ref

Inherit from this class (instead of object) if you want to track live instances with the trackref module.

scrapy.utils.trackref.print_live_refs(class_name, ignore=NoneType)

Print a report of live references, grouped by class name.

Parameters:ignore (class or classes tuple) – if given, all objects from the specified class (or tuple of classes) will be ignored.
scrapy.utils.trackref.get_oldest(class_name)

Return the oldest object alive with the given class name, or None if none is found. Use print_live_refs() first to get a list of all tracked live objects per class name.

scrapy.utils.trackref.iter_all(class_name)

Return an iterator over all objects alive with the given class name, or None if none is found. Use print_live_refs() first to get a list of all tracked live objects per class name.

Debugging memory leaks with Guppy

trackref provides a very convenient mechanism for tracking down memory leaks, but it only keeps track of the objects that are more likely to cause memory leaks (Requests, Responses, Items, and Selectors). However, there are other cases where the memory leaks could come from other (more or less obscure) objects. If this is your case, and you can’t find your leaks using trackref, you still have another resource: the Guppy library.

If you use pip, you can install Guppy with the following command:

pip install guppy

The telnet console also comes with a built-in shortcut (hpy) for accessing Guppy heap objects. Here’s an example to view all Python objects available in the heap using Guppy:

>>> x = hpy.heap()
>>> x.bytype
Partition of a set of 297033 objects. Total size = 52587824 bytes.
 Index  Count   %     Size   % Cumulative  % Type
     0  22307   8 16423880  31  16423880  31 dict
     1 122285  41 12441544  24  28865424  55 str
     2  68346  23  5966696  11  34832120  66 tuple
     3    227   0  5836528  11  40668648  77 unicode
     4   2461   1  2222272   4  42890920  82 type
     5  16870   6  2024400   4  44915320  85 function
     6  13949   5  1673880   3  46589200  89 types.CodeType
     7  13422   5  1653104   3  48242304  92 list
     8   3735   1  1173680   2  49415984  94 _sre.SRE_Pattern
     9   1209   0   456936   1  49872920  95 scrapy.http.headers.Headers
<1676 more rows. Type e.g. '_.more' to view.>

You can see that most space is used by dicts. Then, if you want to see from which attribute those dicts are referenced, you could do:

>>> x.bytype[0].byvia
Partition of a set of 22307 objects. Total size = 16423880 bytes.
 Index  Count   %     Size   % Cumulative  % Referred Via:
     0  10982  49  9416336  57   9416336  57 '.__dict__'
     1   1820   8  2681504  16  12097840  74 '.__dict__', '.func_globals'
     2   3097  14  1122904   7  13220744  80
     3    990   4   277200   2  13497944  82 "['cookies']"
     4    987   4   276360   2  13774304  84 "['cache']"
     5    985   4   275800   2  14050104  86 "['meta']"
     6    897   4   251160   2  14301264  87 '[2]'
     7      1   0   196888   1  14498152  88 "['moduleDict']", "['modules']"
     8    672   3   188160   1  14686312  89 "['cb_kwargs']"
     9     27   0   155016   1  14841328  90 '[1]'
<333 more rows. Type e.g. '_.more' to view.>

As you can see, the Guppy module is very powerful but also requires some deep knowledge about Python internals. For more info about Guppy, refer to the Guppy documentation.

Leaks without leaks

Sometimes, you may notice that the memory usage of your Scrapy process will only increase, but never decrease. Unfortunately, this could happen even though neither Scrapy nor your project are leaking memory. This is due to a (not so well) known problem of Python, which may not return released memory to the operating system in some cases. For more information on this issue see:

The improvements proposed by Evan Jones, which are detailed in this paper, got merged in Python 2.5, but this only reduces the problem, it doesn’t fix it completely. To quote the paper:

Unfortunately, this patch can only free an arena if there are no more objects allocated in it anymore. This means that fragmentation is a large issue. An application could have many megabytes of free memory, scattered throughout all the arenas, but it will be unable to free any of it. This is a problem experienced by all memory allocators. The only way to solve it is to move to a compacting garbage collector, which is able to move objects in memory. This would require significant changes to the Python interpreter.

To keep memory consumption reasonable you can split the job into several smaller jobs or enable persistent job queue and stop/start spider from time to time.

Downloading and processing files and images

Scrapy provides reusable item pipelines for downloading files attached to a particular item (for example, when you scrape products and also want to download their images locally). These pipelines share a bit of functionality and structure (we refer to them as media pipelines), but typically you’ll either use the Files Pipeline or the Images Pipeline.

Both pipelines implement these features:

  • Avoid re-downloading media that was downloaded recently
  • Specifying where to store the media (filesystem directory, Amazon S3 bucket)

The Images Pipeline has a few extra functions for processing images:

  • Convert all downloaded images to a common format (JPG) and mode (RGB)
  • Thumbnail generation
  • Check images width/height to make sure they meet a minimum constraint

The pipelines also keep an internal queue of those media URLs which are currently being scheduled for download, and connect those responses that arrive containing the same media to that queue. This avoids downloading the same media more than once when it’s shared by several items.

Using the Files Pipeline

The typical workflow, when using the FilesPipeline goes like this:

  1. In a Spider, you scrape an item and put the URLs of the desired into a file_urls field.
  2. The item is returned from the spider and goes to the item pipeline.
  3. When the item reaches the FilesPipeline, the URLs in the file_urls field are scheduled for download using the standard Scrapy scheduler and downloader (which means the scheduler and downloader middlewares are reused), but with a higher priority, processing them before other pages are scraped. The item remains “locked” at that particular pipeline stage until the files have finish downloading (or fail for some reason).
  4. When the files are downloaded, another field (files) will be populated with the results. This field will contain a list of dicts with information about the downloaded files, such as the downloaded path, the original scraped url (taken from the file_urls field) , and the file checksum. The files in the list of the files field will retain the same order of the original file_urls field. If some file failed downloading, an error will be logged and the file won’t be present in the files field.

Using the Images Pipeline

Using the ImagesPipeline is a lot like using the FilesPipeline, except the default field names used are different: you use image_urls for the image URLs of an item and it will populate an images field for the information about the downloaded images.

The advantage of using the ImagesPipeline for image files is that you can configure some extra functions like generating thumbnails and filtering the images based on their size.

The Images Pipeline uses Pillow for thumbnailing and normalizing images to JPEG/RGB format, so you need to install this library in order to use it. Python Imaging Library (PIL) should also work in most cases, but it is known to cause troubles in some setups, so we recommend to use Pillow instead of PIL.

Usage example

In order to use a media pipeline first, enable it.

Then, if a spider returns a dict with the URLs key (‘file_urls’ or ‘image_urls’, for the Files or Images Pipeline respectively), the pipeline will put the results under respective key (‘files’ or images’).

If you prefer to use Item, then define a custom item with the necessary fields, like in this example for Images Pipeline:

import scrapy

class MyItem(scrapy.Item):

    # ... other item fields ...
    image_urls = scrapy.Field()
    images = scrapy.Field()

If you need something more complex and want to override the custom pipeline behaviour, see Extending the Media Pipelines.

Enabling your Media Pipeline

To enable your media pipeline you must first add it to your project ITEM_PIPELINES setting.

For Images Pipeline, use:

ITEM_PIPELINES = {'scrapy.pipelines.images.ImagesPipeline': 1}

For Files Pipeline, use:

ITEM_PIPELINES = {'scrapy.pipelines.files.FilesPipeline': 1}

Note

You can also use both the Files and Images Pipeline at the same time.

Then, configure the target storage setting to a valid value that will be used for storing the downloaded images. Otherwise the pipeline will remain disabled, even if you include it in the ITEM_PIPELINES setting.

For the Files Pipeline, set the FILES_STORE setting:

FILES_STORE = '/path/to/valid/dir'

For the Images Pipeline, set the IMAGES_STORE setting:

IMAGES_STORE = '/path/to/valid/dir'

Supported Storage

File system is currently the only officially supported storage, but there is also (undocumented) support for storing files in Amazon S3.

File system storage

The files are stored using a SHA1 hash of their URLs for the file names.

For example, the following image URL:

http://www.example.com/image.jpg

Whose SHA1 hash is:

3afec3b4765f8f0a07b78f98c07b83f013567a0a

Will be downloaded and stored in the following file:

<IMAGES_STORE>/full/3afec3b4765f8f0a07b78f98c07b83f013567a0a.jpg

Where:

  • <IMAGES_STORE> is the directory defined in IMAGES_STORE setting for the Images Pipeline.
  • full is a sub-directory to separate full images from thumbnails (if used). For more info see Thumbnail generation for images.

Additional features

File expiration

The Image Pipeline avoids downloading files that were downloaded recently. To adjust this retention delay use the FILES_EXPIRES setting (or IMAGES_EXPIRES, in case of Images Pipeline), which specifies the delay in number of days:

# 90 days of delay for files expiration
FILES_EXPIRES = 90

# 30 days of delay for images expiration
IMAGES_EXPIRES = 30
Thumbnail generation for images

The Images Pipeline can automatically create thumbnails of the downloaded images.

In order use this feature, you must set IMAGES_THUMBS to a dictionary where the keys are the thumbnail names and the values are their dimensions.

For example:

IMAGES_THUMBS = {
    'small': (50, 50),
    'big': (270, 270),
}

When you use this feature, the Images Pipeline will create thumbnails of the each specified size with this format:

<IMAGES_STORE>/thumbs/<size_name>/<image_id>.jpg

Where:

  • <size_name> is the one specified in the IMAGES_THUMBS dictionary keys (small, big, etc)
  • <image_id> is the SHA1 hash of the image url

Example of image files stored using small and big thumbnail names:

<IMAGES_STORE>/full/63bbfea82b8880ed33cdb762aa11fab722a90a24.jpg
<IMAGES_STORE>/thumbs/small/63bbfea82b8880ed33cdb762aa11fab722a90a24.jpg
<IMAGES_STORE>/thumbs/big/63bbfea82b8880ed33cdb762aa11fab722a90a24.jpg

The first one is the full image, as downloaded from the site.

Filtering out small images

When using the Images Pipeline, you can drop images which are too small, by specifying the minimum allowed size in the IMAGES_MIN_HEIGHT and IMAGES_MIN_WIDTH settings.

For example:

IMAGES_MIN_HEIGHT = 110
IMAGES_MIN_WIDTH = 110

Note: these size constraints don’t affect thumbnail generation at all.

By default, there are no size constraints, so all images are processed.

Extending the Media Pipelines

See here the methods that you can override in your custom Files Pipeline:

class scrapy.pipelines.files.FilesPipeline
get_media_requests(item, info)

As seen on the workflow, the pipeline will get the URLs of the images to download from the item. In order to do this, you can override the get_media_requests() method and return a Request for each file URL:

def get_media_requests(self, item, info):
    for file_url in item['file_urls']:
        yield scrapy.Request(file_url)

Those requests will be processed by the pipeline and, when they have finished downloading, the results will be sent to the item_completed() method, as a list of 2-element tuples. Each tuple will contain (success, file_info_or_error) where:

  • success is a boolean which is True if the image was downloaded successfully or False if it failed for some reason
  • file_info_or_error is a dict containing the following keys (if success is True) or a Twisted Failure if there was a problem.
    • url - the url where the file was downloaded from. This is the url of the request returned from the get_media_requests() method.
    • path - the path (relative to FILES_STORE) where the file was stored
    • checksum - a MD5 hash of the image contents

The list of tuples received by item_completed() is guaranteed to retain the same order of the requests returned from the get_media_requests() method.

Here’s a typical value of the results argument:

[(True,
  {'checksum': '2b00042f7481c7b056c4b410d28f33cf',
   'path': 'full/0a79c461a4062ac383dc4fade7bc09f1384a3910.jpg',
   'url': 'http://www.example.com/files/product1.pdf'}),
 (False,
  Failure(...))]

By default the get_media_requests() method returns None which means there are no files to download for the item.

item_completed(results, items, info)

The FilesPipeline.item_completed() method called when all file requests for a single item have completed (either finished downloading, or failed for some reason).

The item_completed() method must return the output that will be sent to subsequent item pipeline stages, so you must return (or drop) the item, as you would in any pipeline.

Here is an example of the item_completed() method where we store the downloaded file paths (passed in results) in the file_paths item field, and we drop the item if it doesn’t contain any files:

from scrapy.exceptions import DropItem

def item_completed(self, results, item, info):
    file_paths = [x['path'] for ok, x in results if ok]
    if not file_paths:
        raise DropItem("Item contains no files")
    item['file_paths'] = file_paths
    return item

By default, the item_completed() method returns the item.

See here the methods that you can override in your custom Images Pipeline:

class scrapy.pipelines.images.ImagesPipeline
The ImagesPipeline is an extension of the FilesPipeline, customizing the field names and adding custom behavior for images.
get_media_requests(item, info)

Works the same way as FilesPipeline.get_media_requests() method, but using a different field name for image urls.

Must return a Request for each image URL.

item_completed(results, items, info)

The ImagesPipeline.item_completed() method is called when all image requests for a single item have completed (either finished downloading, or failed for some reason).

Works the same way as FilesPipeline.item_completed() method, but using a different field names for storing image downloading results.

By default, the item_completed() method returns the item.

Custom Images pipeline example

Here is a full example of the Images Pipeline whose methods are examplified above:

import scrapy
from scrapy.pipelines.images import ImagesPipeline
from scrapy.exceptions import DropItem

class MyImagesPipeline(ImagesPipeline):

    def get_media_requests(self, item, info):
        for image_url in item['image_urls']:
            yield scrapy.Request(image_url)

    def item_completed(self, results, item, info):
        image_paths = [x['path'] for ok, x in results if ok]
        if not image_paths:
            raise DropItem("Item contains no images")
        item['image_paths'] = image_paths
        return item

Ubuntu packages

New in version 0.10.

Scrapinghub publishes apt-gettable packages which are generally fresher than those in Ubuntu, and more stable too since they’re continuously built from Github repo (master & stable branches) and so they contain the latest bug fixes.

To use the packages:

  1. Import the GPG key used to sign Scrapy packages into APT keyring:

    sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 627220E7

  2. Create /etc/apt/sources.list.d/scrapy.list file using the following command:

    echo 'deb http://archive.scrapy.org/ubuntu scrapy main' | sudo tee /etc/apt/sources.list.d/scrapy.list

  3. Update package lists and install the scrapy package:

    sudo apt-get update && sudo apt-get install scrapy

Note

Repeat step 3 if you are trying to upgrade Scrapy.

Warning

python-scrapy is a different package provided by official debian repositories, it’s very outdated and it isn’t supported by Scrapy team.

Deploying Spiders

This section describes the different options you have for deploying your Scrapy spiders to run them on a regular basis. Running Scrapy spiders in your local machine is very convenient for the (early) development stage, but not so much when you need to execute long-running spiders or move spiders to run in production continuously. This is where the solutions for deploying Scrapy spiders come in.

Popular choices for deploying Scrapy spiders are:

Deploying to a Scrapyd Server

Scrapyd is an open source application to run Scrapy spiders. It provides a server with HTTP API, capable of running and monitoring Scrapy spiders.

To deploy spiders to Scrapyd, you can use the scrapyd-deploy tool provided by the scrapyd-client package. Please refer to the scrapyd-deploy documentation for more information.

Scrapyd is maintained by some of the Scrapy developers.

Deploying to Scrapy Cloud

Scrapy Cloud is a hosted, cloud-based service by Scrapinghub, the company behind Scrapy.

Scrapy Cloud removes the need to setup and monitor servers and provides a nice UI to manage spiders and review scraped items, logs and stats.

To deploy spiders to Scrapy Cloud you can use the shub command line tool. Please refer to the Scrapy Cloud documentation for more information.

Scrapy Cloud is compatible with Scrapyd and one can switch between them as needed - the configuration is read from the scrapy.cfg file just like scrapyd-deploy.

AutoThrottle extension

This is an extension for automatically throttling crawling speed based on load of both the Scrapy server and the website you are crawling.

Design goals

  1. be nicer to sites instead of using default download delay of zero
  2. automatically adjust scrapy to the optimum crawling speed, so the user doesn’t have to tune the download delays and concurrent requests to find the optimum one. The user only needs to specify the maximum concurrent requests it allows, and the extension does the rest.

How it works

In Scrapy, the download latency is measured as the time elapsed between establishing the TCP connection and receiving the HTTP headers.

Note that these latencies are very hard to measure accurately in a cooperative multitasking environment because Scrapy may be busy processing a spider callback, for example, and unable to attend downloads. However, these latencies should still give a reasonable estimate of how busy Scrapy (and ultimately, the server) is, and this extension builds on that premise.

Throttling algorithm

This adjusts download delays and concurrency based on the following rules:

  1. spiders always start with one concurrent request and a download delay of AUTOTHROTTLE_START_DELAY
  2. when a response is received, the download delay is adjusted to the average of previous download delay and the latency of the response.

Note

The AutoThrottle extension honours the standard Scrapy settings for concurrency and delay. This means that it will never set a download delay lower than DOWNLOAD_DELAY or a concurrency higher than CONCURRENT_REQUESTS_PER_DOMAIN (or CONCURRENT_REQUESTS_PER_IP, depending on which one you use).

Settings

The settings used to control the AutoThrottle extension are:

For more information see Throttling algorithm.

AUTOTHROTTLE_ENABLED

Default: False

Enables the AutoThrottle extension.

AUTOTHROTTLE_START_DELAY

Default: 5.0

The initial download delay (in seconds).

AUTOTHROTTLE_MAX_DELAY

Default: 60.0

The maximum download delay (in seconds) to be set in case of high latencies.

AUTOTHROTTLE_DEBUG

Default: False

Enable AutoThrottle debug mode which will display stats on every response received, so you can see how the throttling parameters are being adjusted in real time.

Benchmarking

New in version 0.17.

Scrapy comes with a simple benchmarking suite that spawns a local HTTP server and crawls it at the maximum possible speed. The goal of this benchmarking is to get an idea of how Scrapy performs in your hardware, in order to have a common baseline for comparisons. It uses a simple spider that does nothing and just follows links.

To run it use:

scrapy bench

You should see an output like this:

2013-05-16 13:08:46-0300 [scrapy] INFO: Scrapy 0.17.0 started (bot: scrapybot)
2013-05-16 13:08:47-0300 [scrapy] INFO: Spider opened
2013-05-16 13:08:47-0300 [scrapy] INFO: Crawled 0 pages (at 0 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:48-0300 [scrapy] INFO: Crawled 74 pages (at 4440 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:49-0300 [scrapy] INFO: Crawled 143 pages (at 4140 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:50-0300 [scrapy] INFO: Crawled 210 pages (at 4020 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:51-0300 [scrapy] INFO: Crawled 274 pages (at 3840 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:52-0300 [scrapy] INFO: Crawled 343 pages (at 4140 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:53-0300 [scrapy] INFO: Crawled 410 pages (at 4020 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:54-0300 [scrapy] INFO: Crawled 474 pages (at 3840 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:55-0300 [scrapy] INFO: Crawled 538 pages (at 3840 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:56-0300 [scrapy] INFO: Crawled 602 pages (at 3840 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:57-0300 [scrapy] INFO: Closing spider (closespider_timeout)
2013-05-16 13:08:57-0300 [scrapy] INFO: Crawled 666 pages (at 3840 pages/min), scraped 0 items (at 0 items/min)
2013-05-16 13:08:57-0300 [scrapy] INFO: Dumping Scrapy stats:
    {'downloader/request_bytes': 231508,
     'downloader/request_count': 682,
     'downloader/request_method_count/GET': 682,
     'downloader/response_bytes': 1172802,
     'downloader/response_count': 682,
     'downloader/response_status_count/200': 682,
     'finish_reason': 'closespider_timeout',
     'finish_time': datetime.datetime(2013, 5, 16, 16, 8, 57, 985539),
     'log_count/INFO': 14,
     'request_depth_max': 34,
     'response_received_count': 682,
     'scheduler/dequeued': 682,
     'scheduler/dequeued/memory': 682,
     'scheduler/enqueued': 12767,
     'scheduler/enqueued/memory': 12767,
     'start_time': datetime.datetime(2013, 5, 16, 16, 8, 47, 676539)}
2013-05-16 13:08:57-0300 [scrapy] INFO: Spider closed (closespider_timeout)

That tells you that Scrapy is able to crawl about 3900 pages per minute in the hardware where you run it. Note that this is a very simple spider intended to follow links, any custom spider you write will probably do more stuff which results in slower crawl rates. How slower depends on how much your spider does and how well it’s written.

In the future, more cases will be added to the benchmarking suite to cover other common scenarios.

Jobs: pausing and resuming crawls

Sometimes, for big sites, it’s desirable to pause crawls and be able to resume them later.

Scrapy supports this functionality out of the box by providing the following facilities:

  • a scheduler that persists scheduled requests on disk
  • a duplicates filter that persists visited requests on disk
  • an extension that keeps some spider state (key/value pairs) persistent between batches

Job directory

To enable persistence support you just need to define a job directory through the JOBDIR setting. This directory will be for storing all required data to keep the state of a single job (ie. a spider run). It’s important to note that this directory must not be shared by different spiders, or even different jobs/runs of the same spider, as it’s meant to be used for storing the state of a single job.

How to use it

To start a spider with persistence supported enabled, run it like this:

scrapy crawl somespider -s JOBDIR=crawls/somespider-1

Then, you can stop the spider safely at any time (by pressing Ctrl-C or sending a signal), and resume it later by issuing the same command:

scrapy crawl somespider -s JOBDIR=crawls/somespider-1

Keeping persistent state between batches

Sometimes you’ll want to keep some persistent spider state between pause/resume batches. You can use the spider.state attribute for that, which should be a dict. There’s a built-in extension that takes care of serializing, storing and loading that attribute from the job directory, when the spider starts and stops.

Here’s an example of a callback that uses the spider state (other spider code is omitted for brevity):

def parse_item(self, response):
    # parse item here
    self.state['items_count'] = self.state.get('items_count', 0) + 1

Persistence gotchas

There are a few things to keep in mind if you want to be able to use the Scrapy persistence support:

Cookies expiration

Cookies may expire. So, if you don’t resume your spider quickly the requests scheduled may no longer work. This won’t be an issue if you spider doesn’t rely on cookies.

Request serialization

Requests must be serializable by the pickle module, in order for persistence to work, so you should make sure that your requests are serializable.

The most common issue here is to use lambda functions on request callbacks that can’t be persisted.

So, for example, this won’t work:

def some_callback(self, response):
    somearg = 'test'
    return scrapy.Request('http://www.example.com', callback=lambda r: self.other_callback(r, somearg))

def other_callback(self, response, somearg):
    print "the argument passed is:", somearg

But this will:

def some_callback(self, response):
    somearg = 'test'
    return scrapy.Request('http://www.example.com', callback=self.other_callback, meta={'somearg': somearg})

def other_callback(self, response):
    somearg = response.meta['somearg']
    print "the argument passed is:", somearg
Frequently Asked Questions
Get answers to most frequently asked questions.
Debugging Spiders
Learn how to debug common problems of your scrapy spider.
Spiders Contracts
Learn how to use contracts for testing your spiders.
Common Practices
Get familiar with some Scrapy common practices.
Broad Crawls
Tune Scrapy for crawling a lot domains in parallel.
Using Firefox for scraping
Learn how to scrape with Firefox and some useful add-ons.
Using Firebug for scraping
Learn how to scrape efficiently using Firebug.
Debugging memory leaks
Learn how to find and get rid of memory leaks in your crawler.
Downloading and processing files and images
Download files and/or images associated with your scraped items.
Ubuntu packages
Install latest Scrapy packages easily on Ubuntu
Deploying Spiders
Deploying your Scrapy spiders and run them in a remote server.
AutoThrottle extension
Adjust crawl rate dynamically based on load.
Benchmarking
Check how Scrapy performs on your hardware.
Jobs: pausing and resuming crawls
Learn how to pause and resume crawls for large spiders.

Extending Scrapy

Architecture overview

This document describes the architecture of Scrapy and how its components interact.

Overview

The following diagram shows an overview of the Scrapy architecture with its components and an outline of the data flow that takes place inside the system (shown by the green arrows). A brief description of the components is included below with links for more detailed information about them. The data flow is also described below.

Scrapy architecture

Components

Scrapy Engine

The engine is responsible for controlling the data flow between all components of the system, and triggering events when certain actions occur. See the Data Flow section below for more details.

Scheduler

The Scheduler receives requests from the engine and enqueues them for feeding them later (also to the engine) when the engine requests them.

Downloader

The Downloader is responsible for fetching web pages and feeding them to the engine which, in turn, feeds them to the spiders.

Spiders

Spiders are custom classes written by Scrapy users to parse responses and extract items (aka scraped items) from them or additional URLs (requests) to follow. Each spider is able to handle a specific domain (or group of domains). For more information see Spiders.

Item Pipeline

The Item Pipeline is responsible for processing the items once they have been extracted (or scraped) by the spiders. Typical tasks include cleansing, validation and persistence (like storing the item in a database). For more information see Item Pipeline.

Downloader middlewares

Downloader middlewares are specific hooks that sit between the Engine and the Downloader and process requests when they pass from the Engine to the Downloader, and responses that pass from Downloader to the Engine. They provide a convenient mechanism for extending Scrapy functionality by plugging custom code. For more information see Downloader Middleware.

Spider middlewares

Spider middlewares are specific hooks that sit between the Engine and the Spiders and are able to process spider input (responses) and output (items and requests). They provide a convenient mechanism for extending Scrapy functionality by plugging custom code. For more information see Spider Middleware.

Data flow

The data flow in Scrapy is controlled by the execution engine, and goes like this:

  1. The Engine opens a domain, locates the Spider that handles that domain, and asks the spider for the first URLs to crawl.
  2. The Engine gets the first URLs to crawl from the Spider and schedules them in the Scheduler, as Requests.
  3. The Engine asks the Scheduler for the next URLs to crawl.
  4. The Scheduler returns the next URLs to crawl to the Engine and the Engine sends them to the Downloader, passing through the Downloader Middleware (request direction).
  5. Once the page finishes downloading the Downloader generates a Response (with that page) and sends it to the Engine, passing through the Downloader Middleware (response direction).
  6. The Engine receives the Response from the Downloader and sends it to the Spider for processing, passing through the Spider Middleware (input direction).
  7. The Spider processes the Response and returns scraped items and new Requests (to follow) to the Engine.
  8. The Engine sends scraped items (returned by the Spider) to the Item Pipeline and Requests (returned by spider) to the Scheduler
  9. The process repeats (from step 2) until there are no more requests from the Scheduler, and the Engine closes the domain.

Event-driven networking

Scrapy is written with Twisted, a popular event-driven networking framework for Python. Thus, it’s implemented using a non-blocking (aka asynchronous) code for concurrency.

For more information about asynchronous programming and Twisted see these links:

Downloader Middleware

The downloader middleware is a framework of hooks into Scrapy’s request/response processing. It’s a light, low-level system for globally altering Scrapy’s requests and responses.

Activating a downloader middleware

To activate a downloader middleware component, add it to the DOWNLOADER_MIDDLEWARES setting, which is a dict whose keys are the middleware class paths and their values are the middleware orders.

Here’s an example:

DOWNLOADER_MIDDLEWARES = {
    'myproject.middlewares.CustomDownloaderMiddleware': 543,
}

The DOWNLOADER_MIDDLEWARES setting is merged with the DOWNLOADER_MIDDLEWARES_BASE setting defined in Scrapy (and not meant to be overridden) and then sorted by order to get the final sorted list of enabled middlewares: the first middleware is the one closer to the engine and the last is the one closer to the downloader.

To decide which order to assign to your middleware see the DOWNLOADER_MIDDLEWARES_BASE setting and pick a value according to where you want to insert the middleware. The order does matter because each middleware performs a different action and your middleware could depend on some previous (or subsequent) middleware being applied.

If you want to disable a built-in middleware (the ones defined in DOWNLOADER_MIDDLEWARES_BASE and enabled by default) you must define it in your project’s DOWNLOADER_MIDDLEWARES setting and assign None as its value. For example, if you want to disable the user-agent middleware:

DOWNLOADER_MIDDLEWARES = {
    'myproject.middlewares.CustomDownloaderMiddleware': 543,
    'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware': None,
}

Finally, keep in mind that some middlewares may need to be enabled through a particular setting. See each middleware documentation for more info.

Writing your own downloader middleware

Each middleware component is a Python class that defines one or more of the following methods:

class scrapy.downloadermiddlewares.DownloaderMiddleware
process_request(request, spider)

This method is called for each request that goes through the download middleware.

process_request() should either: return None, return a Response object, return a Request object, or raise IgnoreRequest.

If it returns None, Scrapy will continue processing this request, executing all other middlewares until, finally, the appropriate downloader handler is called the request performed (and its response downloaded).

If it returns a Response object, Scrapy won’t bother calling any other process_request() or process_exception() methods, or the appropriate download function; it’ll return that response. The process_response() methods of installed middleware is always called on every response.

If it returns a Request object, Scrapy will stop calling process_request methods and reschedule the returned request. Once the newly returned request is performed, the appropriate middleware chain will be called on the downloaded response.

If it raises an IgnoreRequest exception, the process_exception() methods of installed downloader middleware will be called. If none of them handle the exception, the errback function of the request (Request.errback) is called. If no code handles the raised exception, it is ignored and not logged (unlike other exceptions).

Parameters:
  • request (Request object) – the request being processed
  • spider (Spider object) – the spider for which this request is intended
process_response(request, response, spider)

process_response() should either: return a Response object, return a Request object or raise a IgnoreRequest exception.

If it returns a Response (it could be the same given response, or a brand-new one), that response will continue to be processed with the process_response() of the next middleware in the chain.

If it returns a Request object, the middleware chain is halted and the returned request is rescheduled to be downloaded in the future. This is the same behavior as if a request is returned from process_request().

If it raises an IgnoreRequest exception, the errback function of the request (Request.errback) is called. If no code handles the raised exception, it is ignored and not logged (unlike other exceptions).

Parameters:
  • request (is a Request object) – the request that originated the response
  • response (Response object) – the response being processed
  • spider (Spider object) – the spider for which this response is intended
process_exception(request, exception, spider)

Scrapy calls process_exception() when a download handler or a process_request() (from a downloader middleware) raises an exception (including an IgnoreRequest exception)

process_exception() should return: either None, a Response object, or a Request object.

If it returns None, Scrapy will continue processing this exception, executing any other process_exception() methods of installed middleware, until no middleware is left and the default exception handling kicks in.

If it returns a Response object, the process_response() method chain of installed middleware is started, and Scrapy won’t bother calling any other process_exception() methods of middleware.

If it returns a Request object, the returned request is rescheduled to be downloaded in the future. This stops the execution of process_exception() methods of the middleware the same as returning a response would.

Parameters:
  • request (is a Request object) – the request that generated the exception
  • exception (an Exception object) – the raised exception
  • spider (Spider object) – the spider for which this request is intended

Built-in downloader middleware reference

This page describes all downloader middleware components that come with Scrapy. For information on how to use them and how to write your own downloader middleware, see the downloader middleware usage guide.

For a list of the components enabled by default (and their orders) see the DOWNLOADER_MIDDLEWARES_BASE setting.

CookiesMiddleware
class scrapy.downloadermiddlewares.cookies.CookiesMiddleware

This middleware enables working with sites that require cookies, such as those that use sessions. It keeps track of cookies sent by web servers, and send them back on subsequent requests (from that spider), just like web browsers do.

The following settings can be used to configure the cookie middleware:

COOKIES_ENABLED

Default: True

Whether to enable the cookies middleware. If disabled, no cookies will be sent to web servers.

COOKIES_DEBUG

Default: False

If enabled, Scrapy will log all cookies sent in requests (ie. Cookie header) and all cookies received in responses (ie. Set-Cookie header).

Here’s an example of a log with COOKIES_DEBUG enabled:

2011-04-06 14:35:10-0300 [scrapy] INFO: Spider opened
2011-04-06 14:35:10-0300 [scrapy] DEBUG: Sending cookies to: <GET http://www.diningcity.com/netherlands/index.html>
        Cookie: clientlanguage_nl=en_EN
2011-04-06 14:35:14-0300 [scrapy] DEBUG: Received cookies from: <200 http://www.diningcity.com/netherlands/index.html>
        Set-Cookie: JSESSIONID=B~FA4DC0C496C8762AE4F1A620EAB34F38; Path=/
        Set-Cookie: ip_isocode=US
        Set-Cookie: clientlanguage_nl=en_EN; Expires=Thu, 07-Apr-2011 21:21:34 GMT; Path=/
2011-04-06 14:49:50-0300 [scrapy] DEBUG: Crawled (200) <GET http://www.diningcity.com/netherlands/index.html> (referer: None)
[...]
DefaultHeadersMiddleware
class scrapy.downloadermiddlewares.defaultheaders.DefaultHeadersMiddleware

This middleware sets all default requests headers specified in the DEFAULT_REQUEST_HEADERS setting.

DownloadTimeoutMiddleware
class scrapy.downloadermiddlewares.downloadtimeout.DownloadTimeoutMiddleware

This middleware sets the download timeout for requests specified in the DOWNLOAD_TIMEOUT setting or download_timeout spider attribute.

Note

You can also set download timeout per-request using download_timeout Request.meta key; this is supported even when DownloadTimeoutMiddleware is disabled.

HttpAuthMiddleware
class scrapy.downloadermiddlewares.httpauth.HttpAuthMiddleware

This middleware authenticates all requests generated from certain spiders using Basic access authentication (aka. HTTP auth).

To enable HTTP authentication from certain spiders, set the http_user and http_pass attributes of those spiders.

Example:

from scrapy.spiders import CrawlSpider

class SomeIntranetSiteSpider(CrawlSpider):

    http_user = 'someuser'
    http_pass = 'somepass'
    name = 'intranet.example.com'

    # .. rest of the spider code omitted ...
HttpCacheMiddleware
class scrapy.downloadermiddlewares.httpcache.HttpCacheMiddleware

This middleware provides low-level cache to all HTTP requests and responses. It has to be combined with a cache storage backend as well as a cache policy.

Scrapy ships with two HTTP cache storage backends:

You can change the HTTP cache storage backend with the HTTPCACHE_STORAGE setting. Or you can also implement your own storage backend.

Scrapy ships with two HTTP cache policies:

You can change the HTTP cache policy with the HTTPCACHE_POLICY setting. Or you can also implement your own policy.

You can also avoid caching a response on every policy using dont_cache meta key equals True.

Dummy policy (default)

This policy has no awareness of any HTTP Cache-Control directives. Every request and its corresponding response are cached. When the same request is seen again, the response is returned without transferring anything from the Internet.

The Dummy policy is useful for testing spiders faster (without having to wait for downloads every time) and for trying your spider offline, when an Internet connection is not available. The goal is to be able to “replay” a spider run exactly as it ran before.

In order to use this policy, set:

RFC2616 policy

This policy provides a RFC2616 compliant HTTP cache, i.e. with HTTP Cache-Control awareness, aimed at production and used in continuous runs to avoid downloading unmodified data (to save bandwidth and speed up crawls).

what is implemented:

  • Do not attempt to store responses/requests with no-store cache-control directive set
  • Do not serve responses from cache if no-cache cache-control directive is set even for fresh responses
  • Compute freshness lifetime from max-age cache-control directive
  • Compute freshness lifetime from Expires response header
  • Compute freshness lifetime from Last-Modified response header (heuristic used by Firefox)
  • Compute current age from Age response header
  • Compute current age from Date header
  • Revalidate stale responses based on Last-Modified response header
  • Revalidate stale responses based on ETag response header
  • Set Date header for any received response missing it

what is missing:

In order to use this policy, set:

Filesystem storage backend (default)

File system storage backend is available for the HTTP cache middleware.

In order to use this storage backend, set:

Each request/response pair is stored in a different directory containing the following files:

  • request_body - the plain request body
  • request_headers - the request headers (in raw HTTP format)
  • response_body - the plain response body
  • response_headers - the request headers (in raw HTTP format)
  • meta - some metadata of this cache resource in Python repr() format (grep-friendly format)
  • pickled_meta - the same metadata in meta but pickled for more efficient deserialization

The directory name is made from the request fingerprint (see scrapy.utils.request.fingerprint), and one level of subdirectories is used to avoid creating too many files into the same directory (which is inefficient in many file systems). An example directory could be:

/path/to/cache/dir/example.com/72/72811f648e718090f041317756c03adb0ada46c7
DBM storage backend

New in version 0.13.

A DBM storage backend is also available for the HTTP cache middleware.

By default, it uses the anydbm module, but you can change it with the HTTPCACHE_DBM_MODULE setting.

In order to use this storage backend, set:

LevelDB storage backend

New in version 0.23.

A LevelDB storage backend is also available for the HTTP cache middleware.

This backend is not recommended for development because only one process can access LevelDB databases at the same time, so you can’t run a crawl and open the scrapy shell in parallel for the same spider.

In order to use this storage backend:

HTTPCache middleware settings

The HttpCacheMiddleware can be configured through the following settings:

HTTPCACHE_ENABLED

New in version 0.11.

Default: False

Whether the HTTP cache will be enabled.

Changed in version 0.11: Before 0.11, HTTPCACHE_DIR was used to enable cache.

HTTPCACHE_EXPIRATION_SECS

Default: 0

Expiration time for cached requests, in seconds.

Cached requests older than this time will be re-downloaded. If zero, cached requests will never expire.

Changed in version 0.11: Before 0.11, zero meant cached requests always expire.

HTTPCACHE_DIR

Default: 'httpcache'

The directory to use for storing the (low-level) HTTP cache. If empty, the HTTP cache will be disabled. If a relative path is given, is taken relative to the project data dir. For more info see: Default structure of Scrapy projects.

HTTPCACHE_IGNORE_HTTP_CODES

New in version 0.10.

Default: []

Don’t cache response with these HTTP codes.

HTTPCACHE_IGNORE_MISSING

Default: False

If enabled, requests not found in the cache will be ignored instead of downloaded.

HTTPCACHE_IGNORE_SCHEMES

New in version 0.10.

Default: ['file']

Don’t cache responses with these URI schemes.

HTTPCACHE_STORAGE

Default: 'scrapy.extensions.httpcache.FilesystemCacheStorage'

The class which implements the cache storage backend.

HTTPCACHE_DBM_MODULE

New in version 0.13.

Default: 'anydbm'

The database module to use in the DBM storage backend. This setting is specific to the DBM backend.

HTTPCACHE_POLICY

New in version 0.18.

Default: 'scrapy.extensions.httpcache.DummyPolicy'

The class which implements the cache policy.

HTTPCACHE_GZIP

New in version 0.25.

Default: False

If enabled, will compress all cached data with gzip. This setting is specific to the Filesystem backend.

HttpCompressionMiddleware
class scrapy.downloadermiddlewares.httpcompression.HttpCompressionMiddleware

This middleware allows compressed (gzip, deflate) traffic to be sent/received from web sites.

HttpCompressionMiddleware Settings
COMPRESSION_ENABLED

Default: True

Whether the Compression middleware will be enabled.

ChunkedTransferMiddleware
class scrapy.downloadermiddlewares.chunked.ChunkedTransferMiddleware

This middleware adds support for chunked transfer encoding

HttpProxyMiddleware

New in version 0.8.

class scrapy.downloadermiddlewares.httpproxy.HttpProxyMiddleware

This middleware sets the HTTP proxy to use for requests, by setting the proxy meta value for Request objects.

Like the Python standard library modules urllib and urllib2, it obeys the following environment variables:

  • http_proxy
  • https_proxy
  • no_proxy

You can also set the meta key proxy per-request, to a value like http://some_proxy_server:port.

RedirectMiddleware
class scrapy.downloadermiddlewares.redirect.RedirectMiddleware

This middleware handles redirection of requests based on response status.

The urls which the request goes through (while being redirected) can be found in the redirect_urls Request.meta key.

The RedirectMiddleware can be configured through the following settings (see the settings documentation for more info):

If Request.meta has dont_redirect key set to True, the request will be ignored by this middleware.

RedirectMiddleware settings
REDIRECT_ENABLED

New in version 0.13.

Default: True

Whether the Redirect middleware will be enabled.

REDIRECT_MAX_TIMES

Default: 20

The maximum number of redirections that will be follow for a single request.

MetaRefreshMiddleware
class scrapy.downloadermiddlewares.redirect.MetaRefreshMiddleware

This middleware handles redirection of requests based on meta-refresh html tag.

The MetaRefreshMiddleware can be configured through the following settings (see the settings documentation for more info):

This middleware obey REDIRECT_MAX_TIMES setting, dont_redirect and redirect_urls request meta keys as described for RedirectMiddleware

MetaRefreshMiddleware settings
METAREFRESH_ENABLED

New in version 0.17.

Default: True

Whether the Meta Refresh middleware will be enabled.

REDIRECT_MAX_METAREFRESH_DELAY

Default: 100

The maximum meta-refresh delay (in seconds) to follow the redirection.

RetryMiddleware
class scrapy.downloadermiddlewares.retry.RetryMiddleware

A middleware to retry failed requests that are potentially caused by temporary problems such as a connection timeout or HTTP 500 error.

Failed pages are collected on the scraping process and rescheduled at the end, once the spider has finished crawling all regular (non failed) pages. Once there are no more failed pages to retry, this middleware sends a signal (retry_complete), so other extensions could connect to that signal.

The RetryMiddleware can be configured through the following settings (see the settings documentation for more info):

About HTTP errors to consider:

You may want to remove 400 from RETRY_HTTP_CODES, if you stick to the HTTP protocol. It’s included by default because it’s a common code used to indicate server overload, which would be something we want to retry.

If Request.meta has dont_retry key set to True, the request will be ignored by this middleware.

RetryMiddleware Settings
RETRY_ENABLED

New in version 0.13.

Default: True

Whether the Retry middleware will be enabled.

RETRY_TIMES

Default: 2

Maximum number of times to retry, in addition to the first download.

RETRY_HTTP_CODES

Default: [500, 502, 503, 504, 400, 408]

Which HTTP response codes to retry. Other errors (DNS lookup issues, connections lost, etc) are always retried.

RobotsTxtMiddleware
class scrapy.downloadermiddlewares.robotstxt.RobotsTxtMiddleware

This middleware filters out requests forbidden by the robots.txt exclusion standard.

To make sure Scrapy respects robots.txt make sure the middleware is enabled and the ROBOTSTXT_OBEY setting is enabled.

Warning

Keep in mind that, if you crawl using multiple concurrent requests per domain, Scrapy could still download some forbidden pages if they were requested before the robots.txt file was downloaded. This is a known limitation of the current robots.txt middleware and will be fixed in the future.

If Request.meta has dont_obey_robotstxt key set to True the request will be ignored by this middleware even if ROBOTSTXT_OBEY is enabled.

DownloaderStats
class scrapy.downloadermiddlewares.stats.DownloaderStats

Middleware that stores stats of all requests, responses and exceptions that pass through it.

To use this middleware you must enable the DOWNLOADER_STATS setting.

UserAgentMiddleware
class scrapy.downloadermiddlewares.useragent.UserAgentMiddleware

Middleware that allows spiders to override the default user agent.

In order for a spider to override the default user agent, its user_agent attribute must be set.

AjaxCrawlMiddleware
class scrapy.downloadermiddlewares.ajaxcrawl.AjaxCrawlMiddleware

Middleware that finds ‘AJAX crawlable’ page variants based on meta-fragment html tag. See https://developers.google.com/webmasters/ajax-crawling/docs/getting-started for more info.

Note

Scrapy finds ‘AJAX crawlable’ pages for URLs like 'http://example.com/!#foo=bar' even without this middleware. AjaxCrawlMiddleware is necessary when URL doesn’t contain '!#'. This is often a case for ‘index’ or ‘main’ website pages.

AjaxCrawlMiddleware Settings
AJAXCRAWL_ENABLED

New in version 0.21.

Default: False

Whether the AjaxCrawlMiddleware will be enabled. You may want to enable it for broad crawls.

Spider Middleware

The spider middleware is a framework of hooks into Scrapy’s spider processing mechanism where you can plug custom functionality to process the responses that are sent to Spiders for processing and to process the requests and items that are generated from spiders.

Activating a spider middleware

To activate a spider middleware component, add it to the SPIDER_MIDDLEWARES setting, which is a dict whose keys are the middleware class path and their values are the middleware orders.

Here’s an example:

SPIDER_MIDDLEWARES = {
    'myproject.middlewares.CustomSpiderMiddleware': 543,
}

The SPIDER_MIDDLEWARES setting is merged with the SPIDER_MIDDLEWARES_BASE setting defined in Scrapy (and not meant to be overridden) and then sorted by order to get the final sorted list of enabled middlewares: the first middleware is the one closer to the engine and the last is the one closer to the spider.

To decide which order to assign to your middleware see the SPIDER_MIDDLEWARES_BASE setting and pick a value according to where you want to insert the middleware. The order does matter because each middleware performs a different action and your middleware could depend on some previous (or subsequent) middleware being applied.

If you want to disable a builtin middleware (the ones defined in SPIDER_MIDDLEWARES_BASE, and enabled by default) you must define it in your project SPIDER_MIDDLEWARES setting and assign None as its value. For example, if you want to disable the off-site middleware:

SPIDER_MIDDLEWARES = {
    'myproject.middlewares.CustomSpiderMiddleware': 543,
    'scrapy.spidermiddlewares.offsite.OffsiteMiddleware': None,
}

Finally, keep in mind that some middlewares may need to be enabled through a particular setting. See each middleware documentation for more info.

Writing your own spider middleware

Each middleware component is a Python class that defines one or more of the following methods:

class scrapy.spidermiddlewares.SpiderMiddleware
process_spider_input(response, spider)

This method is called for each response that goes through the spider middleware and into the spider, for processing.

process_spider_input() should return None or raise an exception.

If it returns None, Scrapy will continue processing this response, executing all other middlewares until, finally, the response is handed to the spider for processing.

If it raises an exception, Scrapy won’t bother calling any other spider middleware process_spider_input() and will call the request errback. The output of the errback is chained back in the other direction for process_spider_output() to process it, or process_spider_exception() if it raised an exception.

Parameters:
  • response (Response object) – the response being processed
  • spider (Spider object) – the spider for which this response is intended
process_spider_output(response, result, spider)

This method is called with the results returned from the Spider, after it has processed the response.

process_spider_output() must return an iterable of Request, dict or Item objects.

Parameters:
  • response (Response object) – the response which generated this output from the spider
  • result (an iterable of Request, dict or Item objects) – the result returned by the spider
  • spider (Spider object) – the spider whose result is being processed
process_spider_exception(response, exception, spider)

This method is called when when a spider or process_spider_input() method (from other spider middleware) raises an exception.

process_spider_exception() should return either None or an iterable of Response, dict or Item objects.

If it returns None, Scrapy will continue processing this exception, executing any other process_spider_exception() in the following middleware components, until no middleware components are left and the exception reaches the engine (where it’s logged and discarded).

If it returns an iterable the process_spider_output() pipeline kicks in, and no other process_spider_exception() will be called.

Parameters:
  • response (Response object) – the response being processed when the exception was raised
  • exception (Exception object) – the exception raised
  • spider (Spider object) – the spider which raised the exception
process_start_requests(start_requests, spider)

New in version 0.15.

This method is called with the start requests of the spider, and works similarly to the process_spider_output() method, except that it doesn’t have a response associated and must return only requests (not items).

It receives an iterable (in the start_requests parameter) and must return another iterable of Request objects.

Note

When implementing this method in your spider middleware, you should always return an iterable (that follows the input one) and not consume all start_requests iterator because it can be very large (or even unbounded) and cause a memory overflow. The Scrapy engine is designed to pull start requests while it has capacity to process them, so the start requests iterator can be effectively endless where there is some other condition for stopping the spider (like a time limit or item/page count).

Parameters:
  • start_requests (an iterable of Request) – the start requests
  • spider (Spider object) – the spider to whom the start requests belong

Built-in spider middleware reference

This page describes all spider middleware components that come with Scrapy. For information on how to use them and how to write your own spider middleware, see the spider middleware usage guide.

For a list of the components enabled by default (and their orders) see the SPIDER_MIDDLEWARES_BASE setting.

DepthMiddleware
class scrapy.spidermiddlewares.depth.DepthMiddleware

DepthMiddleware is a scrape middleware used for tracking the depth of each Request inside the site being scraped. It can be used to limit the maximum depth to scrape or things like that.

The DepthMiddleware can be configured through the following settings (see the settings documentation for more info):

  • DEPTH_LIMIT - The maximum depth that will be allowed to crawl for any site. If zero, no limit will be imposed.
  • DEPTH_STATS - Whether to collect depth stats.
  • DEPTH_PRIORITY - Whether to prioritize the requests based on their depth.
HttpErrorMiddleware
class scrapy.spidermiddlewares.httperror.HttpErrorMiddleware

Filter out unsuccessful (erroneous) HTTP responses so that spiders don’t have to deal with them, which (most of the time) imposes an overhead, consumes more resources, and makes the spider logic more complex.

According to the HTTP standard, successful responses are those whose status codes are in the 200-300 range.

If you still want to process response codes outside that range, you can specify which response codes the spider is able to handle using the handle_httpstatus_list spider attribute or HTTPERROR_ALLOWED_CODES setting.

For example, if you want your spider to handle 404 responses you can do this:

class MySpider(CrawlSpider):
    handle_httpstatus_list = [404]

The handle_httpstatus_list key of Request.meta can also be used to specify which response codes to allow on a per-request basis. You can also set the meta key handle_httpstatus_all to True if you want to allow any response code for a request.

Keep in mind, however, that it’s usually a bad idea to handle non-200 responses, unless you really know what you’re doing.

For more information see: HTTP Status Code Definitions.

HttpErrorMiddleware settings
HTTPERROR_ALLOWED_CODES

Default: []

Pass all responses with non-200 status codes contained in this list.

HTTPERROR_ALLOW_ALL

Default: False

Pass all responses, regardless of its status code.

OffsiteMiddleware
class scrapy.spidermiddlewares.offsite.OffsiteMiddleware

Filters out Requests for URLs outside the domains covered by the spider.

This middleware filters out every request whose host names aren’t in the spider’s allowed_domains attribute.

When your spider returns a request for a domain not belonging to those covered by the spider, this middleware will log a debug message similar to this one:

DEBUG: Filtered offsite request to 'www.othersite.com': <GET http://www.othersite.com/some/page.html>

To avoid filling the log with too much noise, it will only print one of these messages for each new domain filtered. So, for example, if another request for www.othersite.com is filtered, no log message will be printed. But if a request for someothersite.com is filtered, a message will be printed (but only for the first request filtered).

If the spider doesn’t define an allowed_domains attribute, or the attribute is empty, the offsite middleware will allow all requests.

If the request has the dont_filter attribute set, the offsite middleware will allow the request even if its domain is not listed in allowed domains.

RefererMiddleware
class scrapy.spidermiddlewares.referer.RefererMiddleware

Populates Request Referer header, based on the URL of the Response which generated it.

RefererMiddleware settings
REFERER_ENABLED

New in version 0.15.

Default: True

Whether to enable referer middleware.

UrlLengthMiddleware
class scrapy.spidermiddlewares.urllength.UrlLengthMiddleware

Filters out requests with URLs longer than URLLENGTH_LIMIT

The UrlLengthMiddleware can be configured through the following settings (see the settings documentation for more info):

Extensions

The extensions framework provides a mechanism for inserting your own custom functionality into Scrapy.

Extensions are just regular classes that are instantiated at Scrapy startup, when extensions are initialized.

Extension settings

Extensions use the Scrapy settings to manage their settings, just like any other Scrapy code.

It is customary for extensions to prefix their settings with their own name, to avoid collision with existing (and future) extensions. For example, a hypothetic extension to handle Google Sitemaps would use settings like GOOGLESITEMAP_ENABLED, GOOGLESITEMAP_DEPTH, and so on.

Loading & activating extensions

Extensions are loaded and activated at startup by instantiating a single instance of the extension class. Therefore, all the extension initialization code must be performed in the class constructor (__init__ method).

To make an extension available, add it to the EXTENSIONS setting in your Scrapy settings. In EXTENSIONS, each extension is represented by a string: the full Python path to the extension’s class name. For example:

EXTENSIONS = {
    'scrapy.extensions.corestats.CoreStats': 500,
    'scrapy.telnet.TelnetConsole': 500,
}

As you can see, the EXTENSIONS setting is a dict where the keys are the extension paths, and their values are the orders, which define the extension loading order. Extensions orders are not as important as middleware orders though, and they are typically irrelevant, ie. it doesn’t matter in which order the extensions are loaded because they don’t depend on each other [1].

However, this feature can be exploited if you need to add an extension which depends on other extensions already loaded.

[1] This is is why the EXTENSIONS_BASE setting in Scrapy (which contains all built-in extensions enabled by default) defines all the extensions with the same order (500).

Available, enabled and disabled extensions

Not all available extensions will be enabled. Some of them usually depend on a particular setting. For example, the HTTP Cache extension is available by default but disabled unless the HTTPCACHE_ENABLED setting is set.

Disabling an extension

In order to disable an extension that comes enabled by default (ie. those included in the EXTENSIONS_BASE setting) you must set its order to None. For example:

EXTENSIONS = {
    'scrapy.extensions.corestats.CoreStats': None,
}

Writing your own extension

Each extension is a Python class. The main entry point for a Scrapy extension (this also includes middlewares and pipelines) is the from_crawler class method which receives a Crawler instance. Through the Crawler object you can access settings, signals, stats, and also control the crawling behaviour.

Typically, extensions connect to signals and perform tasks triggered by them.

Finally, if the from_crawler method raises the NotConfigured exception, the extension will be disabled. Otherwise, the extension will be enabled.

Sample extension

Here we will implement a simple extension to illustrate the concepts described in the previous section. This extension will log a message every time:

  • a spider is opened
  • a spider is closed
  • a specific number of items are scraped

The extension will be enabled through the MYEXT_ENABLED setting and the number of items will be specified through the MYEXT_ITEMCOUNT setting.

Here is the code of such extension:

import logging
from scrapy import signals
from scrapy.exceptions import NotConfigured

logger = logging.getLogger(__name__)

class SpiderOpenCloseLogging(object):

    def __init__(self, item_count):
        self.item_count = item_count
        self.items_scraped = 0

    @classmethod
    def from_crawler(cls, crawler):
        # first check if the extension should be enabled and raise
        # NotConfigured otherwise
        if not crawler.settings.getbool('MYEXT_ENABLED'):
            raise NotConfigured

        # get the number of items from settings
        item_count = crawler.settings.getint('MYEXT_ITEMCOUNT', 1000)

        # instantiate the extension object
        ext = cls(item_count)

        # connect the extension object to signals
        crawler.signals.connect(ext.spider_opened, signal=signals.spider_opened)
        crawler.signals.connect(ext.spider_closed, signal=signals.spider_closed)
        crawler.signals.connect(ext.item_scraped, signal=signals.item_scraped)

        # return the extension object
        return ext

    def spider_opened(self, spider):
        logger.info("opened spider %s", spider.name)

    def spider_closed(self, spider):
        logger.info("closed spider %s", spider.name)

    def item_scraped(self, item, spider):
        self.items_scraped += 1
        if self.items_scraped % self.item_count == 0:
            logger.info("scraped %d items", self.items_scraped)

Built-in extensions reference

General purpose extensions
Log Stats extension
class scrapy.extensions.logstats.LogStats

Log basic stats like crawled pages and scraped items.

Core Stats extension
class scrapy.extensions.corestats.CoreStats

Enable the collection of core statistics, provided the stats collection is enabled (see Stats Collection).

Telnet console extension
class scrapy.telnet.TelnetConsole

Provides a telnet console for getting into a Python interpreter inside the currently running Scrapy process, which can be very useful for debugging.

The telnet console must be enabled by the TELNETCONSOLE_ENABLED setting, and the server will listen in the port specified in TELNETCONSOLE_PORT.

Memory usage extension
class scrapy.extensions.memusage.MemoryUsage

Note

This extension does not work in Windows.

Monitors the memory used by the Scrapy process that runs the spider and:

  1. sends a notification e-mail when it exceeds a certain value
  2. closes the spider when it exceeds a certain value

The notification e-mails can be triggered when a certain warning value is reached (MEMUSAGE_WARNING_MB) and when the maximum value is reached (MEMUSAGE_LIMIT_MB) which will also cause the spider to be closed and the Scrapy process to be terminated.

This extension is enabled by the MEMUSAGE_ENABLED setting and can be configured with the following settings:

Memory debugger extension
class scrapy.extensions.memdebug.MemoryDebugger

An extension for debugging memory usage. It collects information about:

To enable this extension, turn on the MEMDEBUG_ENABLED setting. The info will be stored in the stats.

Close spider extension
class scrapy.extensions.closespider.CloseSpider

Closes a spider automatically when some conditions are met, using a specific closing reason for each condition.

The conditions for closing a spider can be configured through the following settings:

CLOSESPIDER_TIMEOUT

Default: 0

An integer which specifies a number of seconds. If the spider remains open for more than that number of second, it will be automatically closed with the reason closespider_timeout. If zero (or non set), spiders won’t be closed by timeout.

CLOSESPIDER_ITEMCOUNT

Default: 0

An integer which specifies a number of items. If the spider scrapes more than that amount if items and those items are passed by the item pipeline, the spider will be closed with the reason closespider_itemcount. If zero (or non set), spiders won’t be closed by number of passed items.

CLOSESPIDER_PAGECOUNT

New in version 0.11.

Default: 0

An integer which specifies the maximum number of responses to crawl. If the spider crawls more than that, the spider will be closed with the reason closespider_pagecount. If zero (or non set), spiders won’t be closed by number of crawled responses.

CLOSESPIDER_ERRORCOUNT

New in version 0.11.

Default: 0

An integer which specifies the maximum number of errors to receive before closing the spider. If the spider generates more than that number of errors, it will be closed with the reason closespider_errorcount. If zero (or non set), spiders won’t be closed by number of errors.

StatsMailer extension
class scrapy.extensions.statsmailer.StatsMailer

This simple extension can be used to send a notification e-mail every time a domain has finished scraping, including the Scrapy stats collected. The email will be sent to all recipients specified in the STATSMAILER_RCPTS setting.

Debugging extensions
Stack trace dump extension
class scrapy.extensions.debug.StackTraceDump

Dumps information about the running process when a SIGQUIT or SIGUSR2 signal is received. The information dumped is the following:

  1. engine status (using scrapy.utils.engine.get_engine_status())
  2. live references (see Debugging memory leaks with trackref)
  3. stack trace of all threads

After the stack trace and engine status is dumped, the Scrapy process continues running normally.

This extension only works on POSIX-compliant platforms (ie. not Windows), because the SIGQUIT and SIGUSR2 signals are not available on Windows.

There are at least two ways to send Scrapy the SIGQUIT signal:

  1. By pressing Ctrl-while a Scrapy process is running (Linux only?)

  2. By running this command (assuming <pid> is the process id of the Scrapy process):

    kill -QUIT <pid>
Debugger extension
class scrapy.extensions.debug.Debugger

Invokes a Python debugger inside a running Scrapy process when a SIGUSR2 signal is received. After the debugger is exited, the Scrapy process continues running normally.

For more info see Debugging in Python.

This extension only works on POSIX-compliant platforms (ie. not Windows).

Core API

New in version 0.15.

This section documents the Scrapy core API, and it’s intended for developers of extensions and middlewares.

Crawler API

The main entry point to Scrapy API is the Crawler object, passed to extensions through the from_crawler class method. This object provides access to all Scrapy core components, and it’s the only way for extensions to access them and hook their functionality into Scrapy.

The Extension Manager is responsible for loading and keeping track of installed extensions and it’s configured through the EXTENSIONS setting which contains a dictionary of all available extensions and their order similar to how you configure the downloader middlewares.

class scrapy.crawler.Crawler(spidercls, settings)

The Crawler object must be instantiated with a scrapy.spiders.Spider subclass and a scrapy.settings.Settings object.

settings

The settings manager of this crawler.

This is used by extensions & middlewares to access the Scrapy settings of this crawler.

For an introduction on Scrapy settings see Settings.

For the API see Settings class.

signals

The signals manager of this crawler.

This is used by extensions & middlewares to hook themselves into Scrapy functionality.

For an introduction on signals see Signals.

For the API see SignalManager class.

stats

The stats collector of this crawler.

This is used from extensions & middlewares to record stats of their behaviour, or access stats collected by other extensions.

For an introduction on stats collection see Stats Collection.

For the API see StatsCollector class.

extensions

The extension manager that keeps track of enabled extensions.

Most extensions won’t need to access this attribute.

For an introduction on extensions and a list of available extensions on Scrapy see Extensions.

engine

The execution engine, which coordinates the core crawling logic between the scheduler, downloader and spiders.

Some extension may want to access the Scrapy engine, to inspect or modify the downloader and scheduler behaviour, although this is an advanced use and this API is not yet stable.

spider

Spider currently being crawled. This is an instance of the spider class provided while constructing the crawler, and it is created after the arguments given in the crawl() method.

crawl(*args, **kwargs)

Starts the crawler by instantiating its spider class with the given args and kwargs arguments, while setting the execution engine in motion.

Returns a deferred that is fired when the crawl is finished.

class scrapy.crawler.CrawlerRunner(settings=None)

This is a convenient helper class that keeps track of, manages and runs crawlers inside an already setup Twisted reactor.

The CrawlerRunner object must be instantiated with a Settings object.

This class shouldn’t be needed (since Scrapy is responsible of using it accordingly) unless writing scripts that manually handle the crawling process. See Run Scrapy from a script for an example.

crawl(crawler_or_spidercls, *args, **kwargs)

Run a crawler with the provided arguments.

It will call the given Crawler’s crawl() method, while keeping track of it so it can be stopped later.

If crawler_or_spidercls isn’t a Crawler instance, this method will try to create one using this parameter as the spider class given to it.

Returns a deferred that is fired when the crawling is finished.

Parameters:
  • crawler_or_spidercls (Crawler instance, Spider subclass or string) – already created crawler, or a spider class or spider’s name inside the project to create it
  • args (list) – arguments to initialize the spider
  • kwargs (dict) – keyword arguments to initialize the spider
crawlers

Set of crawlers started by crawl() and managed by this class.

join()

Returns a deferred that is fired when all managed crawlers have completed their executions.

stop()

Stops simultaneously all the crawling jobs taking place.

Returns a deferred that is fired when they all have ended.

class scrapy.crawler.CrawlerProcess(settings=None)

Bases: scrapy.crawler.CrawlerRunner

A class to run multiple scrapy crawlers in a process simultaneously.

This class extends CrawlerRunner by adding support for starting a Twisted reactor and handling shutdown signals, like the keyboard interrupt command Ctrl-C. It also configures top-level logging.

This utility should be a better fit than CrawlerRunner if you aren’t running another Twisted reactor within your application.

The CrawlerProcess object must be instantiated with a Settings object.

This class shouldn’t be needed (since Scrapy is responsible of using it accordingly) unless writing scripts that manually handle the crawling process. See Run Scrapy from a script for an example.

crawl(crawler_or_spidercls, *args, **kwargs)

Run a crawler with the provided arguments.

It will call the given Crawler’s crawl() method, while keeping track of it so it can be stopped later.

If crawler_or_spidercls isn’t a Crawler instance, this method will try to create one using this parameter as the spider class given to it.

Returns a deferred that is fired when the crawling is finished.

Parameters:
  • crawler_or_spidercls (Crawler instance, Spider subclass or string) – already created crawler, or a spider class or spider’s name inside the project to create it
  • args (list) – arguments to initialize the spider
  • kwargs (dict) – keyword arguments to initialize the spider
crawlers

Set of crawlers started by crawl() and managed by this class.

join()

Returns a deferred that is fired when all managed crawlers have completed their executions.

start(stop_after_crawl=True)

This method starts a Twisted reactor, adjusts its pool size to REACTOR_THREADPOOL_MAXSIZE, and installs a DNS cache based on DNSCACHE_ENABLED and DNSCACHE_SIZE.

If stop_after_crawl is True, the reactor will be stopped after all crawlers have finished, using join().

Parameters:stop_after_crawl (boolean) – stop or not the reactor when all crawlers have finished
stop()

Stops simultaneously all the crawling jobs taking place.

Returns a deferred that is fired when they all have ended.

Settings API

scrapy.settings.SETTINGS_PRIORITIES

Dictionary that sets the key name and priority level of the default settings priorities used in Scrapy.

Each item defines a settings entry point, giving it a code name for identification and an integer priority. Greater priorities take more precedence over lesser ones when setting and retrieving values in the Settings class.

SETTINGS_PRIORITIES = {
    'default': 0,
    'command': 10,
    'project': 20,
    'spider': 30,
    'cmdline': 40,
}

For a detailed explanation on each settings sources, see: Settings.

class scrapy.settings.Settings(values={}, priority='project')

This object stores Scrapy settings for the configuration of internal components, and can be used for any further customization.

After instantiation of this class, the new object will have the global default settings described on Built-in settings reference already populated.

Additional values can be passed on initialization with the values argument, and they would take the priority level. If the latter argument is a string, the priority name will be looked up in SETTINGS_PRIORITIES. Otherwise, a expecific integer should be provided.

Once the object is created, new settings can be loaded or updated with the set() method, and can be accessed with the square bracket notation of dictionaries, or with the get() method of the instance and its value conversion variants. When requesting a stored key, the value with the highest priority will be retrieved.

set(name, value, priority='project')

Store a key/value attribute with a given priority.

Settings should be populated before configuring the Crawler object (through the configure() method), otherwise they won’t have any effect.

Parameters:
  • name (string) – the setting name
  • value (any) – the value to associate with the setting
  • priority (string or int) – the priority of the setting. Should be a key of SETTINGS_PRIORITIES or an integer
setdict(values, priority='project')

Store key/value pairs with a given priority.

This is a helper function that calls set() for every item of values with the provided priority.

Parameters:
  • values (dict) – the settings names and values
  • priority (string or int) – the priority of the settings. Should be a key of SETTINGS_PRIORITIES or an integer
setmodule(module, priority='project')

Store settings from a module with a given priority.

This is a helper function that calls set() for every globally declared uppercase variable of module with the provided priority.

Parameters:
  • module (module object or string) – the module or the path of the module
  • priority (string or int) – the priority of the settings. Should be a key of SETTINGS_PRIORITIES or an integer
get(name, default=None)

Get a setting value without affecting its original type.

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
getbool(name, default=False)

Get a setting value as a boolean. For example, both 1 and '1', and True return True, while 0, '0', False and None return False``

For example, settings populated through environment variables set to '0' will return False when using this method.

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
getint(name, default=0)

Get a setting value as an int

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
getfloat(name, default=0.0)

Get a setting value as a float

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
getlist(name, default=None)

Get a setting value as a list. If the setting original type is a list, a copy of it will be returned. If it’s a string it will be split by ”,”.

For example, settings populated through environment variables set to 'one,two' will return a list [‘one’, ‘two’] when using this method.

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
getdict(name, default=None)

Get a setting value as a dictionary. If the setting original type is a dictionary, a copy of it will be returned. If it’s a string it will evaluated as a json dictionary.

Parameters:
  • name (string) – the setting name
  • default (any) – the value to return if no setting is found
copy()

Make a deep copy of current settings.

This method returns a new instance of the Settings class, populated with the same values and their priorities.

Modifications to the new object won’t be reflected on the original settings.

freeze()

Disable further changes to the current settings.

After calling this method, the present state of the settings will become immutable. Trying to change values through the set() method and its variants won’t be possible and will be alerted.

frozencopy()

Return an immutable copy of the current settings.

Alias for a freeze() call in the object returned by copy()

SpiderLoader API

class scrapy.loader.SpiderLoader

This class is in charge of retrieving and handling the spider classes defined across the project.

Custom spider loaders can be employed by specifying their path in the SPIDER_LOADER_CLASS project setting. They must fully implement the scrapy.interfaces.ISpiderLoader interface to guarantee an errorless execution.

from_settings(settings)

This class method is used by Scrapy to create an instance of the class. It’s called with the current project settings, and it loads the spiders found in the modules of the SPIDER_MODULES setting.

Parameters:settings (Settings instance) – project settings
load(spider_name)

Get the Spider class with the given name. It’ll look into the previously loaded spiders for a spider class with name spider_name and will raise a KeyError if not found.

Parameters:spider_name (str) – spider class name
list()

Get the names of the available spiders in the project.

find_by_request(request)

List the spiders’ names that can handle the given request. Will try to match the request’s url against the domains of the spiders.

Parameters:request (Request instance) – queried request

Signals API

class scrapy.signalmanager.SignalManager
connect(receiver, signal)

Connect a receiver function to a signal.

The signal can be any object, although Scrapy comes with some predefined signals that are documented in the Signals section.

Parameters:
  • receiver (callable) – the function to be connected
  • signal (object) – the signal to connect to
send_catch_log(signal, **kwargs)

Send a signal, catch exceptions and log them.

The keyword arguments are passed to the signal handlers (connected through the connect() method).

send_catch_log_deferred(signal, **kwargs)

Like send_catch_log() but supports returning deferreds from signal handlers.

Returns a deferred that gets fired once all signal handlers deferreds were fired. Send a signal, catch exceptions and log them.

The keyword arguments are passed to the signal handlers (connected through the connect() method).

disconnect(receiver, signal)

Disconnect a receiver function from a signal. This has the opposite effect of the connect() method, and the arguments are the same.

disconnect_all(signal)

Disconnect all receivers from the given signal.

Parameters:signal (object) – the signal to disconnect from

Stats Collector API

There are several Stats Collectors available under the scrapy.statscollectors module and they all implement the Stats Collector API defined by the StatsCollector class (which they all inherit from).

class scrapy.statscollectors.StatsCollector
get_value(key, default=None)

Return the value for the given stats key or default if it doesn’t exist.

get_stats()

Get all stats from the currently running spider as a dict.

set_value(key, value)

Set the given value for the given stats key.

set_stats(stats)

Override the current stats with the dict passed in stats argument.

inc_value(key, count=1, start=0)

Increment the value of the given stats key, by the given count, assuming the start value given (when it’s not set).

max_value(key, value)

Set the given value for the given key only if current value for the same key is lower than value. If there is no current value for the given key, the value is always set.

min_value(key, value)

Set the given value for the given key only if current value for the same key is greater than value. If there is no current value for the given key, the value is always set.

clear_stats()

Clear all stats.

The following methods are not part of the stats collection api but instead used when implementing custom stats collectors:

open_spider(spider)

Open the given spider for stats collection.

close_spider(spider)

Close the given spider. After this is called, no more specific stats can be accessed or collected.

Signals

Scrapy uses signals extensively to notify when certain events occur. You can catch some of those signals in your Scrapy project (using an extension, for example) to perform additional tasks or extend Scrapy to add functionality not provided out of the box.

Even though signals provide several arguments, the handlers that catch them don’t need to accept all of them - the signal dispatching mechanism will only deliver the arguments that the handler receives.

You can connect to signals (or send your own) through the Signals API.

Deferred signal handlers

Some signals support returning Twisted deferreds from their handlers, see the Built-in signals reference below to know which ones.

Built-in signals reference

Here’s the list of Scrapy built-in signals and their meaning.

engine_started
scrapy.signals.engine_started()

Sent when the Scrapy engine has started crawling.

This signal supports returning deferreds from their handlers.

Note

This signal may be fired after the spider_opened signal, depending on how the spider was started. So don’t rely on this signal getting fired before spider_opened.

engine_stopped
scrapy.signals.engine_stopped()

Sent when the Scrapy engine is stopped (for example, when a crawling process has finished).

This signal supports returning deferreds from their handlers.

item_scraped
scrapy.signals.item_scraped(item, response, spider)

Sent when an item has been scraped, after it has passed all the Item Pipeline stages (without being dropped).

This signal supports returning deferreds from their handlers.

Parameters:
  • item (dict or Item object) – the item scraped
  • spider (Spider object) – the spider which scraped the item
  • response (Response object) – the response from where the item was scraped
item_dropped
scrapy.signals.item_dropped(item, response, exception, spider)

Sent after an item has been dropped from the Item Pipeline when some stage raised a DropItem exception.

This signal supports returning deferreds from their handlers.

Parameters:
  • item (dict or Item object) – the item dropped from the Item Pipeline
  • spider (Spider object) – the spider which scraped the item
  • response (Response object) – the response from where the item was dropped
  • exception (DropItem exception) – the exception (which must be a DropItem subclass) which caused the item to be dropped
spider_closed
scrapy.signals.spider_closed(spider, reason)

Sent after a spider has been closed. This can be used to release per-spider resources reserved on spider_opened.

This signal supports returning deferreds from their handlers.

Parameters:
  • spider (Spider object) – the spider which has been closed
  • reason (str) – a string which describes the reason why the spider was closed. If it was closed because the spider has completed scraping, the reason is 'finished'. Otherwise, if the spider was manually closed by calling the close_spider engine method, then the reason is the one passed in the reason argument of that method (which defaults to 'cancelled'). If the engine was shutdown (for example, by hitting Ctrl-C to stop it) the reason will be 'shutdown'.
spider_opened
scrapy.signals.spider_opened(spider)

Sent after a spider has been opened for crawling. This is typically used to reserve per-spider resources, but can be used for any task that needs to be performed when a spider is opened.

This signal supports returning deferreds from their handlers.

Parameters:spider (Spider object) – the spider which has been opened
spider_idle
scrapy.signals.spider_idle(spider)

Sent when a spider has gone idle, which means the spider has no further:

  • requests waiting to be downloaded
  • requests scheduled
  • items being processed in the item pipeline

If the idle state persists after all handlers of this signal have finished, the engine starts closing the spider. After the spider has finished closing, the spider_closed signal is sent.

You can, for example, schedule some requests in your spider_idle handler to prevent the spider from being closed.

This signal does not support returning deferreds from their handlers.

Parameters:spider (Spider object) – the spider which has gone idle
spider_error
scrapy.signals.spider_error(failure, response, spider)

Sent when a spider callback generates an error (ie. raises an exception).

This signal does not support returning deferreds from their handlers.

Parameters:
  • failure (Failure object) – the exception raised as a Twisted Failure object
  • response (Response object) – the response being processed when the exception was raised
  • spider (Spider object) – the spider which raised the exception
request_scheduled
scrapy.signals.request_scheduled(request, spider)

Sent when the engine schedules a Request, to be downloaded later.

The signal does not support returning deferreds from their handlers.

Parameters:
  • request (Request object) – the request that reached the scheduler
  • spider (Spider object) – the spider that yielded the request
request_dropped
scrapy.signals.request_dropped(request, spider)

Sent when a Request, scheduled by the engine to be downloaded later, is rejected by the scheduler.

The signal does not support returning deferreds from their handlers.

Parameters:
  • request (Request object) – the request that reached the scheduler
  • spider (Spider object) – the spider that yielded the request
response_received
scrapy.signals.response_received(response, request, spider)

Sent when the engine receives a new Response from the downloader.

This signal does not support returning deferreds from their handlers.

Parameters:
  • response (Response object) – the response received
  • request (Request object) – the request that generated the response
  • spider (Spider object) – the spider for which the response is intended
response_downloaded
scrapy.signals.response_downloaded(response, request, spider)

Sent by the downloader right after a HTTPResponse is downloaded.

This signal does not support returning deferreds from their handlers.

Parameters:
  • response (Response object) – the response downloaded
  • request (Request object) – the request that generated the response
  • spider (Spider object) – the spider for which the response is intended

Item Exporters

Once you have scraped your items, you often want to persist or export those items, to use the data in some other application. That is, after all, the whole purpose of the scraping process.

For this purpose Scrapy provides a collection of Item Exporters for different output formats, such as XML, CSV or JSON.

Using Item Exporters

If you are in a hurry, and just want to use an Item Exporter to output scraped data see the Feed exports. Otherwise, if you want to know how Item Exporters work or need more custom functionality (not covered by the default exports), continue reading below.

In order to use an Item Exporter, you must instantiate it with its required args. Each Item Exporter requires different arguments, so check each exporter documentation to be sure, in Built-in Item Exporters reference. After you have instantiated your exporter, you have to:

1. call the method start_exporting() in order to signal the beginning of the exporting process

2. call the export_item() method for each item you want to export

3. and finally call the finish_exporting() to signal the end of the exporting process

Here you can see an Item Pipeline which uses an Item Exporter to export scraped items to different files, one per spider:

from scrapy import signals
from scrapy.exporters import XmlItemExporter

class XmlExportPipeline(object):

    def __init__(self):
        self.files = {}

     @classmethod
     def from_crawler(cls, crawler):
         pipeline = cls()
         crawler.signals.connect(pipeline.spider_opened, signals.spider_opened)
         crawler.signals.connect(pipeline.spider_closed, signals.spider_closed)
         return pipeline

    def spider_opened(self, spider):
        file = open('%s_products.xml' % spider.name, 'w+b')
        self.files[spider] = file
        self.exporter = XmlItemExporter(file)
        self.exporter.start_exporting()

    def spider_closed(self, spider):
        self.exporter.finish_exporting()
        file = self.files.pop(spider)
        file.close()

    def process_item(self, item, spider):
        self.exporter.export_item(item)
        return item

Serialization of item fields

By default, the field values are passed unmodified to the underlying serialization library, and the decision of how to serialize them is delegated to each particular serialization library.

However, you can customize how each field value is serialized before it is passed to the serialization library.

There are two ways to customize how a field will be serialized, which are described next.

1. Declaring a serializer in the field

If you use Item you can declare a serializer in the field metadata. The serializer must be a callable which receives a value and returns its serialized form.

Example:

import scrapy

def serialize_price(value):
    return '$ %s' % str(value)

class Product(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field(serializer=serialize_price)
2. Overriding the serialize_field() method

You can also override the serialize_field() method to customize how your field value will be exported.

Make sure you call the base class serialize_field() method after your custom code.

Example:

from scrapy.exporter import XmlItemExporter

class ProductXmlExporter(XmlItemExporter):

    def serialize_field(self, field, name, value):
        if field == 'price':
            return '$ %s' % str(value)
        return super(Product, self).serialize_field(field, name, value)

Built-in Item Exporters reference

Here is a list of the Item Exporters bundled with Scrapy. Some of them contain output examples, which assume you’re exporting these two items:

Item(name='Color TV', price='1200')
Item(name='DVD player', price='200')
BaseItemExporter
class scrapy.exporters.BaseItemExporter(fields_to_export=None, export_empty_fields=False, encoding='utf-8')

This is the (abstract) base class for all Item Exporters. It provides support for common features used by all (concrete) Item Exporters, such as defining what fields to export, whether to export empty fields, or which encoding to use.

These features can be configured through the constructor arguments which populate their respective instance attributes: fields_to_export, export_empty_fields, encoding.

export_item(item)

Exports the given item. This method must be implemented in subclasses.

serialize_field(field, name, value)

Return the serialized value for the given field. You can override this method (in your custom Item Exporters) if you want to control how a particular field or value will be serialized/exported.

By default, this method looks for a serializer declared in the item field and returns the result of applying that serializer to the value. If no serializer is found, it returns the value unchanged except for unicode values which are encoded to str using the encoding declared in the encoding attribute.

Parameters:
  • field (Field object or an empty dict) – the field being serialized. If a raw dict is being exported (not Item) field value is an empty dict.
  • name (str) – the name of the field being serialized
  • value – the value being serialized
start_exporting()

Signal the beginning of the exporting process. Some exporters may use this to generate some required header (for example, the XmlItemExporter). You must call this method before exporting any items.

finish_exporting()

Signal the end of the exporting process. Some exporters may use this to generate some required footer (for example, the XmlItemExporter). You must always call this method after you have no more items to export.

fields_to_export

A list with the name of the fields that will be exported, or None if you want to export all fields. Defaults to None.

Some exporters (like CsvItemExporter) respect the order of the fields defined in this attribute.

Some exporters may require fields_to_export list in order to export the data properly when spiders return dicts (not Item instances).

export_empty_fields

Whether to include empty/unpopulated item fields in the exported data. Defaults to False. Some exporters (like CsvItemExporter) ignore this attribute and always export all empty fields.

This option is ignored for dict items.

encoding

The encoding that will be used to encode unicode values. This only affects unicode values (which are always serialized to str using this encoding). Other value types are passed unchanged to the specific serialization library.

XmlItemExporter
class scrapy.exporters.XmlItemExporter(file, item_element='item', root_element='items', **kwargs)

Exports Items in XML format to the specified file object.

Parameters:
  • file – the file-like object to use for exporting the data.
  • root_element (str) – The name of root element in the exported XML.
  • item_element (str) – The name of each item element in the exported XML.

The additional keyword arguments of this constructor are passed to the BaseItemExporter constructor.

A typical output of this exporter would be:

<?xml version="1.0" encoding="utf-8"?>
<items>
  <item>
    <name>Color TV</name>
    <price>1200</price>
 </item>
  <item>
    <name>DVD player</name>
    <price>200</price>
 </item>
</items>

Unless overridden in the serialize_field() method, multi-valued fields are exported by serializing each value inside a <value> element. This is for convenience, as multi-valued fields are very common.

For example, the item:

Item(name=['John', 'Doe'], age='23')

Would be serialized as:

<?xml version="1.0" encoding="utf-8"?>
<items>
  <item>
    <name>
      <value>John</value>
      <value>Doe</value>
    </name>
    <age>23</age>
  </item>
</items>
CsvItemExporter
class scrapy.exporters.CsvItemExporter(file, include_headers_line=True, join_multivalued=', ', **kwargs)

Exports Items in CSV format to the given file-like object. If the fields_to_export attribute is set, it will be used to define the CSV columns and their order. The export_empty_fields attribute has no effect on this exporter.

Parameters:
  • file – the file-like object to use for exporting the data.
  • include_headers_line (str) – If enabled, makes the exporter output a header line with the field names taken from BaseItemExporter.fields_to_export or the first exported item fields.
  • join_multivalued – The char (or chars) that will be used for joining multi-valued fields, if found.

The additional keyword arguments of this constructor are passed to the BaseItemExporter constructor, and the leftover arguments to the csv.writer constructor, so you can use any csv.writer constructor argument to customize this exporter.

A typical output of this exporter would be:

product,price
Color TV,1200
DVD player,200
PickleItemExporter
class scrapy.exporters.PickleItemExporter(file, protocol=0, **kwargs)

Exports Items in pickle format to the given file-like object.

Parameters:
  • file – the file-like object to use for exporting the data.
  • protocol (int) – The pickle protocol to use.

For more information, refer to the pickle module documentation.

The additional keyword arguments of this constructor are passed to the BaseItemExporter constructor.

Pickle isn’t a human readable format, so no output examples are provided.

PprintItemExporter
class scrapy.exporters.PprintItemExporter(file, **kwargs)

Exports Items in pretty print format to the specified file object.

Parameters:file – the file-like object to use for exporting the data.

The additional keyword arguments of this constructor are passed to the BaseItemExporter constructor.

A typical output of this exporter would be:

{'name': 'Color TV', 'price': '1200'}
{'name': 'DVD player', 'price': '200'}

Longer lines (when present) are pretty-formatted.

JsonItemExporter
class scrapy.exporters.JsonItemExporter(file, **kwargs)

Exports Items in JSON format to the specified file-like object, writing all objects as a list of objects. The additional constructor arguments are passed to the BaseItemExporter constructor, and the leftover arguments to the JSONEncoder constructor, so you can use any JSONEncoder constructor argument to customize this exporter.

Parameters:file – the file-like object to use for exporting the data.

A typical output of this exporter would be:

[{"name": "Color TV", "price": "1200"},
{"name": "DVD player", "price": "200"}]

Warning

JSON is very simple and flexible serialization format, but it doesn’t scale well for large amounts of data since incremental (aka. stream-mode) parsing is not well supported (if at all) among JSON parsers (on any language), and most of them just parse the entire object in memory. If you want the power and simplicity of JSON with a more stream-friendly format, consider using JsonLinesItemExporter instead, or splitting the output in multiple chunks.

JsonLinesItemExporter
class scrapy.exporters.JsonLinesItemExporter(file, **kwargs)

Exports Items in JSON format to the specified file-like object, writing one JSON-encoded item per line. The additional constructor arguments are passed to the BaseItemExporter constructor, and the leftover arguments to the JSONEncoder constructor, so you can use any JSONEncoder constructor argument to customize this exporter.

Parameters:file – the file-like object to use for exporting the data.

A typical output of this exporter would be:

{"name": "Color TV", "price": "1200"}
{"name": "DVD player", "price": "200"}

Unlike the one produced by JsonItemExporter, the format produced by this exporter is well suited for serializing large amounts of data.

Architecture overview
Understand the Scrapy architecture.
Downloader Middleware
Customize how pages get requested and downloaded.
Spider Middleware
Customize the input and output of your spiders.
Extensions
Extend Scrapy with your custom functionality
Core API
Use it on extensions and middlewares to extend Scrapy functionality
Signals
See all available signals and how to work with them.
Item Exporters
Quickly export your scraped items to a file (XML, CSV, etc).

All the rest

Release notes

1.0.7 (2017-03-03)

  • Packaging fix: disallow unsupported Twisted versions in setup.py

1.0.6 (2016-05-04)

  • FIX: RetryMiddleware is now robust to non-standard HTTP status codes (issue 1857)
  • FIX: Filestorage HTTP cache was checking wrong modified time (issue 1875)
  • DOC: Support for Sphinx 1.4+ (issue 1893)
  • DOC: Consistency in selectors examples (issue 1869)

1.0.5 (2016-02-04)

1.0.4 (2015-12-30)

1.0.3 (2015-08-11)

1.0.2 (2015-08-06)

1.0.1 (2015-07-01)

1.0.0 (2015-06-19)

You will find a lot of new features and bugfixes in this major release. Make sure to check our updated overview to get a glance of some of the changes, along with our brushed tutorial.

Support for returning dictionaries in spiders

Declaring and returning Scrapy Items is no longer necessary to collect the scraped data from your spider, you can now return explicit dictionaries instead.

Classic version

class MyItem(scrapy.Item):
    url = scrapy.Field()

class MySpider(scrapy.Spider):
    def parse(self, response):
        return MyItem(url=response.url)

New version

class MySpider(scrapy.Spider):
    def parse(self, response):
        return {'url': response.url}
Per-spider settings (GSoC 2014)

Last Google Summer of Code project accomplished an important redesign of the mechanism used for populating settings, introducing explicit priorities to override any given setting. As an extension of that goal, we included a new level of priority for settings that act exclusively for a single spider, allowing them to redefine project settings.

Start using it by defining a custom_settings class variable in your spider:

class MySpider(scrapy.Spider):
    custom_settings = {
        "DOWNLOAD_DELAY": 5.0,
        "RETRY_ENABLED": False,
    }

Read more about settings population: Settings

Python Logging

Scrapy 1.0 has moved away from Twisted logging to support Python built in’s as default logging system. We’re maintaining backward compatibility for most of the old custom interface to call logging functions, but you’ll get warnings to switch to the Python logging API entirely.

Old version

from scrapy import log
log.msg('MESSAGE', log.INFO)

New version

import logging
logging.info('MESSAGE')

Logging with spiders remains the same, but on top of the log() method you’ll have access to a custom logger created for the spider to issue log events:

class MySpider(scrapy.Spider):
    def parse(self, response):
        self.logger.info('Response received')

Read more in the logging documentation: Logging

Crawler API refactoring (GSoC 2014)

Another milestone for last Google Summer of Code was a refactoring of the internal API, seeking a simpler and easier usage. Check new core interface in: Core API

A common situation where you will face these changes is while running Scrapy from scripts. Here’s a quick example of how to run a Spider manually with the new API:

from scrapy.crawler import CrawlerProcess

process = CrawlerProcess({
    'USER_AGENT': 'Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1)'
})
process.crawl(MySpider)
process.start()

Bear in mind this feature is still under development and its API may change until it reaches a stable status.

See more examples for scripts running Scrapy: Common Practices

Module Relocations

There’s been a large rearrangement of modules trying to improve the general structure of Scrapy. Main changes were separating various subpackages into new projects and dissolving both scrapy.contrib and scrapy.contrib_exp into top level packages. Backward compatibility was kept among internal relocations, while importing deprecated modules expect warnings indicating their new place.

Full list of relocations

Outsourced packages

Note

These extensions went through some minor changes, e.g. some setting names were changed. Please check the documentation in each new repository to get familiar with the new usage.

Old location New location
scrapy.commands.deploy scrapyd-client (See other alternatives here: Deploying Spiders)
scrapy.contrib.djangoitem scrapy-djangoitem
scrapy.webservice scrapy-jsonrpc

scrapy.contrib_exp and scrapy.contrib dissolutions

Old location New location
scrapy.contrib_exp.downloadermiddleware.decompression scrapy.downloadermiddlewares.decompression
scrapy.contrib_exp.iterators scrapy.utils.iterators
scrapy.contrib.downloadermiddleware scrapy.downloadermiddlewares
scrapy.contrib.exporter scrapy.exporters
scrapy.contrib.linkextractors scrapy.linkextractors
scrapy.contrib.loader scrapy.loader
scrapy.contrib.loader.processor scrapy.loader.processors
scrapy.contrib.pipeline scrapy.pipelines
scrapy.contrib.spidermiddleware scrapy.spidermiddlewares
scrapy.contrib.spiders scrapy.spiders
  • scrapy.contrib.closespider
  • scrapy.contrib.corestats
  • scrapy.contrib.debug
  • scrapy.contrib.feedexport
  • scrapy.contrib.httpcache
  • scrapy.contrib.logstats
  • scrapy.contrib.memdebug
  • scrapy.contrib.memusage
  • scrapy.contrib.spiderstate
  • scrapy.contrib.statsmailer
  • scrapy.contrib.throttle
 scrapy.extensions.*

Plural renames and Modules unification

Old location New location
scrapy.command scrapy.commands
scrapy.dupefilter scrapy.dupefilters
scrapy.linkextractor scrapy.linkextractors
scrapy.spider scrapy.spiders
scrapy.squeue scrapy.squeues
scrapy.statscol scrapy.statscollectors
scrapy.utils.decorator scrapy.utils.decorators

Class renames

Old location New location
scrapy.spidermanager.SpiderManager scrapy.spiderloader.SpiderLoader

Settings renames

Old location New location
SPIDER_MANAGER_CLASS SPIDER_LOADER_CLASS
Changelog

New Features and Enhancements

Deprecations and Removals

  • Deprecate htmlparser link extractor (issue 1205)
  • remove deprecated code from FeedExporter (issue 1155)
  • a leftover for.15 compatibility (issue 925)
  • drop support for CONCURRENT_REQUESTS_PER_SPIDER (issue 895)
  • Drop old engine code (issue 911)
  • Deprecate SgmlLinkExtractor (issue 777)

Relocations

Documentation

Bugfixes

  • Item multi inheritance fix (issue 353, issue 1228)
  • ItemLoader.load_item: iterate over copy of fields (issue 722)
  • Fix Unhandled error in Deferred (RobotsTxtMiddleware) (issue 1131, issue 1197)
  • Force to read DOWNLOAD_TIMEOUT as int (issue 954)
  • scrapy.utils.misc.load_object should print full traceback (issue 902)
  • Fix bug for ”.local” host name (issue 878)
  • Fix for Enabled extensions, middlewares, pipelines info not printed anymore (issue 879)
  • fix dont_merge_cookies bad behaviour when set to false on meta (issue 846)

Python 3 In Progress Support

  • disable scrapy.telnet if twisted.conch is not available (issue 1161)
  • fix Python 3 syntax errors in ajaxcrawl.py (issue 1162)
  • more python3 compatibility changes for urllib (issue 1121)
  • assertItemsEqual was renamed to assertCountEqual in Python 3. (issue 1070)
  • Import unittest.mock if available. (issue 1066)
  • updated deprecated cgi.parse_qsl to use six’s parse_qsl (issue 909)
  • Prevent Python 3 port regressions (issue 830)
  • PY3: use MutableMapping for python 3 (issue 810)
  • PY3: use six.BytesIO and six.moves.cStringIO (issue 803)
  • PY3: fix xmlrpclib and email imports (issue 801)
  • PY3: use six for robotparser and urlparse (issue 800)
  • PY3: use six.iterkeys, six.iteritems, and tempfile (issue 799)
  • PY3: fix has_key and use six.moves.configparser (issue 798)
  • PY3: use six.moves.cPickle (issue 797)
  • PY3 make it possible to run some tests in Python3 (issue 776)

Tests

  • remove unnecessary lines from py3-ignores (issue 1243)
  • Fix remaining warnings from pytest while collecting tests (issue 1206)
  • Add docs build to travis (issue 1234)
  • TST don’t collect tests from deprecated modules. (issue 1165)
  • install service_identity package in tests to prevent warnings (issue 1168)
  • Fix deprecated settings API in tests (issue 1152)
  • Add test for webclient with POST method and no body given (issue 1089)
  • py3-ignores.txt supports comments (issue 1044)
  • modernize some of the asserts (issue 835)
  • selector.__repr__ test (issue 779)

Code refactoring

  • CSVFeedSpider cleanup: use iterate_spider_output (issue 1079)
  • remove unnecessary check from scrapy.utils.spider.iter_spider_output (issue 1078)
  • Pydispatch pep8 (issue 992)
  • Removed unused ‘load=False’ parameter from walk_modules() (issue 871)
  • For consistency, use job_dir helper in SpiderState extension. (issue 805)
  • rename “sflo” local variables to less cryptic “log_observer” (issue 775)

0.24.6 (2015-04-20)

0.24.5 (2015-02-25)

0.24.4 (2014-08-09)

0.24.3 (2014-08-09)

0.24.2 (2014-07-08)

  • Use a mutable mapping to proxy deprecated settings.overrides and settings.defaults attribute (commit e5e8133)
  • there is not support for python3 yet (commit 3cd6146)
  • Update python compatible version set to debian packages (commit fa5d76b)
  • DOC fix formatting in release notes (commit c6a9e20)

0.24.1 (2014-06-27)

  • Fix deprecated CrawlerSettings and increase backwards compatibility with .defaults attribute (commit 8e3f20a)

0.24.0 (2014-06-26)

Enhancements
Bugfixes
  • Encode unicode URL value when creating Links in RegexLinkExtractor (issue 561)
  • Ignore None values in ItemLoader processors (issue 556)
  • Fix link text when there is an inner tag in SGMLLinkExtractor and HtmlParserLinkExtractor (issue 485, issue 574)
  • Fix wrong checks on subclassing of deprecated classes (issue 581, issue 584)
  • Handle errors caused by inspect.stack() failures (issue 582)
  • Fix a reference to unexistent engine attribute (issue 593, issue 594)
  • Fix dynamic itemclass example usage of type() (issue 603)
  • Use lucasdemarchi/codespell to fix typos (issue 628)
  • Fix default value of attrs argument in SgmlLinkExtractor to be tuple (issue 661)
  • Fix XXE flaw in sitemap reader (issue 676)
  • Fix engine to support filtered start requests (issue 707)
  • Fix offsite middleware case on urls with no hostnames (issue 745)
  • Testsuite doesn’t require PIL anymore (issue 585)

0.22.2 (released 2014-02-14)

0.22.1 (released 2014-02-08)

  • localhost666 can resolve under certain circumstances (commit 2ec2279)
  • test inspect.stack failure (commit cc3eda3)
  • Handle cases when inspect.stack() fails (commit 8cb44f9)
  • Fix wrong checks on subclassing of deprecated classes. closes #581 (commit 46d98d6)
  • Docs: 4-space indent for final spider example (commit 13846de)
  • Fix HtmlParserLinkExtractor and tests after #485 merge (commit 368a946)
  • BaseSgmlLinkExtractor: Fixed the missing space when the link has an inner tag (commit b566388)
  • BaseSgmlLinkExtractor: Added unit test of a link with an inner tag (commit c1cb418)
  • BaseSgmlLinkExtractor: Fixed unknown_endtag() so that it only set current_link=None when the end tag match the opening tag (commit 7e4d627)
  • Fix tests for Travis-CI build (commit 76c7e20)
  • replace unencodeable codepoints with html entities. fixes #562 and #285 (commit 5f87b17)
  • RegexLinkExtractor: encode URL unicode value when creating Links (commit d0ee545)
  • Updated the tutorial crawl output with latest output. (commit 8da65de)
  • Updated shell docs with the crawler reference and fixed the actual shell output. (commit 875b9ab)
  • PEP8 minor edits. (commit f89efaf)
  • Expose current crawler in the scrapy shell. (commit 5349cec)
  • Unused re import and PEP8 minor edits. (commit 387f414)
  • Ignore None’s values when using the ItemLoader. (commit 0632546)
  • DOC Fixed HTTPCACHE_STORAGE typo in the default value which is now Filesystem instead Dbm. (commit cde9a8c)
  • show ubuntu setup instructions as literal code (commit fb5c9c5)
  • Update Ubuntu installation instructions (commit 70fb105)
  • Merge pull request #550 from stray-leone/patch-1 (commit 6f70b6a)
  • modify the version of scrapy ubuntu package (commit 725900d)
  • fix 0.22.0 release date (commit af0219a)
  • fix typos in news.rst and remove (not released yet) header (commit b7f58f4)

0.22.0 (released 2014-01-17)

Enhancements
  • [Backwards incompatible] Switched HTTPCacheMiddleware backend to filesystem (issue 541) To restore old backend set HTTPCACHE_STORAGE to scrapy.contrib.httpcache.DbmCacheStorage
  • Proxy https:// urls using CONNECT method (issue 392, issue 397)
  • Add a middleware to crawl ajax crawleable pages as defined by google (issue 343)
  • Rename scrapy.spider.BaseSpider to scrapy.spider.Spider (issue 510, issue 519)
  • Selectors register EXSLT namespaces by default (issue 472)
  • Unify item loaders similar to selectors renaming (issue 461)
  • Make RFPDupeFilter class easily subclassable (issue 533)
  • Improve test coverage and forthcoming Python 3 support (issue 525)
  • Promote startup info on settings and middleware to INFO level (issue 520)
  • Support partials in get_func_args util (issue 506, issue:504)
  • Allow running indiviual tests via tox (issue 503)
  • Update extensions ignored by link extractors (issue 498)
  • Add middleware methods to get files/images/thumbs paths (issue 490)
  • Improve offsite middleware tests (issue 478)
  • Add a way to skip default Referer header set by RefererMiddleware (issue 475)
  • Do not send x-gzip in default Accept-Encoding header (issue 469)
  • Support defining http error handling using settings (issue 466)
  • Use modern python idioms wherever you find legacies (issue 497)
  • Improve and correct documentation (issue 527, issue 524, issue 521, issue 517, issue 512, issue 505, issue 502, issue 489, issue 465, issue 460, issue 425, issue 536)
Fixes
  • Update Selector class imports in CrawlSpider template (issue 484)
  • Fix unexistent reference to engine.slots (issue 464)
  • Do not try to call body_as_unicode() on a non-TextResponse instance (issue 462)
  • Warn when subclassing XPathItemLoader, previously it only warned on instantiation. (issue 523)
  • Warn when subclassing XPathSelector, previously it only warned on instantiation. (issue 537)
  • Multiple fixes to memory stats (issue 531, issue 530, issue 529)
  • Fix overriding url in FormRequest.from_response() (issue 507)
  • Fix tests runner under pip 1.5 (issue 513)
  • Fix logging error when spider name is unicode (issue 479)

0.20.2 (released 2013-12-09)

0.20.1 (released 2013-11-28)

  • include_package_data is required to build wheels from published sources (commit 5ba1ad5)
  • process_parallel was leaking the failures on its internal deferreds. closes #458 (commit 419a780)

0.20.0 (released 2013-11-08)

Enhancements
  • New Selector’s API including CSS selectors (issue 395 and issue 426),
  • Request/Response url/body attributes are now immutable (modifying them had been deprecated for a long time)
  • ITEM_PIPELINES is now defined as a dict (instead of a list)
  • Sitemap spider can fetch alternate URLs (issue 360)
  • Selector.remove_namespaces() now remove namespaces from element’s attributes. (issue 416)
  • Paved the road for Python 3.3+ (issue 435, issue 436, issue 431, issue 452)
  • New item exporter using native python types with nesting support (issue 366)
  • Tune HTTP1.1 pool size so it matches concurrency defined by settings (commit b43b5f575)
  • scrapy.mail.MailSender now can connect over TLS or upgrade using STARTTLS (issue 327)
  • New FilesPipeline with functionality factored out from ImagesPipeline (issue 370, issue 409)
  • Recommend Pillow instead of PIL for image handling (issue 317)
  • Added debian packages for Ubuntu quantal and raring (commit 86230c0)
  • Mock server (used for tests) can listen for HTTPS requests (issue 410)
  • Remove multi spider support from multiple core components (issue 422, issue 421, issue 420, issue 419, issue 423, issue 418)
  • Travis-CI now tests Scrapy changes against development versions of w3lib and queuelib python packages.
  • Add pypy 2.1 to continuous integration tests (commit ecfa7431)
  • Pylinted, pep8 and removed old-style exceptions from source (issue 430, issue 432)
  • Use importlib for parametric imports (issue 445)
  • Handle a regression introduced in Python 2.7.5 that affects XmlItemExporter (issue 372)
  • Bugfix crawling shutdown on SIGINT (issue 450)
  • Do not submit reset type inputs in FormRequest.from_response (commit b326b87)
  • Do not silence download errors when request errback raises an exception (commit 684cfc0)
Bugfixes
Other
  • Dropped Python 2.6 support (issue 448)
  • Add cssselect python package as install dependency
  • Drop libxml2 and multi selector’s backend support, lxml is required from now on.
  • Minimum Twisted version increased to 10.0.0, dropped Twisted 8.0 support.
  • Running test suite now requires mock python library (issue 390)
Thanks

Thanks to everyone who contribute to this release!

List of contributors sorted by number of commits:

69 Daniel Graña <dangra@...>
37 Pablo Hoffman <pablo@...>
13 Mikhail Korobov <kmike84@...>
 9 Alex Cepoi <alex.cepoi@...>
 9 alexanderlukanin13 <alexander.lukanin.13@...>
 8 Rolando Espinoza La fuente <darkrho@...>
 8 Lukasz Biedrycki <lukasz.biedrycki@...>
 6 Nicolas Ramirez <nramirez.uy@...>
 3 Paul Tremberth <paul.tremberth@...>
 2 Martin Olveyra <molveyra@...>
 2 Stefan <misc@...>
 2 Rolando Espinoza <darkrho@...>
 2 Loren Davie <loren@...>
 2 irgmedeiros <irgmedeiros@...>
 1 Stefan Koch <taikano@...>
 1 Stefan <cct@...>
 1 scraperdragon <dragon@...>
 1 Kumara Tharmalingam <ktharmal@...>
 1 Francesco Piccinno <stack.box@...>
 1 Marcos Campal <duendex@...>
 1 Dragon Dave <dragon@...>
 1 Capi Etheriel <barraponto@...>
 1 cacovsky <amarquesferraz@...>
 1 Berend Iwema <berend@...>

0.18.4 (released 2013-10-10)

  • IPython refuses to update the namespace. fix #396 (commit 3d32c4f)
  • Fix AlreadyCalledError replacing a request in shell command. closes #407 (commit b1d8919)
  • Fix start_requests laziness and early hangs (commit 89faf52)

0.18.3 (released 2013-10-03)

0.18.2 (released 2013-09-03)

  • Backport scrapy check command fixes and backward compatible multi crawler process(issue 339)

0.18.1 (released 2013-08-27)

  • remove extra import added by cherry picked changes (commit d20304e)
  • fix crawling tests under twisted pre 11.0.0 (commit 1994f38)
  • py26 can not format zero length fields {} (commit abf756f)
  • test PotentiaDataLoss errors on unbound responses (commit b15470d)
  • Treat responses without content-length or Transfer-Encoding as good responses (commit c4bf324)
  • do no include ResponseFailed if http11 handler is not enabled (commit 6cbe684)
  • New HTTP client wraps connection losts in ResponseFailed exception. fix #373 (commit 1a20bba)
  • limit travis-ci build matrix (commit 3b01bb8)
  • Merge pull request #375 from peterarenot/patch-1 (commit fa766d7)
  • Fixed so it refers to the correct folder (commit 3283809)
  • added quantal & raring to support ubuntu releases (commit 1411923)
  • fix retry middleware which didn’t retry certain connection errors after the upgrade to http1 client, closes GH-373 (commit bb35ed0)
  • fix XmlItemExporter in Python 2.7.4 and 2.7.5 (commit de3e451)
  • minor updates to 0.18 release notes (commit c45e5f1)
  • fix contributters list format (commit 0b60031)

0.18.0 (released 2013-08-09)

  • Lot of improvements to testsuite run using Tox, including a way to test on pypi
  • Handle GET parameters for AJAX crawleable urls (commit 3fe2a32)
  • Use lxml recover option to parse sitemaps (issue 347)
  • Bugfix cookie merging by hostname and not by netloc (issue 352)
  • Support disabling HttpCompressionMiddleware using a flag setting (issue 359)
  • Support xml namespaces using iternodes parser in XMLFeedSpider (issue 12)
  • Support dont_cache request meta flag (issue 19)
  • Bugfix scrapy.utils.gz.gunzip broken by changes in python 2.7.4 (commit 4dc76e)
  • Bugfix url encoding on SgmlLinkExtractor (issue 24)
  • Bugfix TakeFirst processor shouldn’t discard zero (0) value (issue 59)
  • Support nested items in xml exporter (issue 66)
  • Improve cookies handling performance (issue 77)
  • Log dupe filtered requests once (issue 105)
  • Split redirection middleware into status and meta based middlewares (issue 78)
  • Use HTTP1.1 as default downloader handler (issue 109 and issue 318)
  • Support xpath form selection on FormRequest.from_response (issue 185)
  • Bugfix unicode decoding error on SgmlLinkExtractor (issue 199)
  • Bugfix signal dispatching on pypi interpreter (issue 205)
  • Improve request delay and concurrency handling (issue 206)
  • Add RFC2616 cache policy to HttpCacheMiddleware (issue 212)
  • Allow customization of messages logged by engine (issue 214)
  • Multiples improvements to DjangoItem (issue 217, issue 218, issue 221)
  • Extend Scrapy commands using setuptools entry points (issue 260)
  • Allow spider allowed_domains value to be set/tuple (issue 261)
  • Support settings.getdict (issue 269)
  • Simplify internal scrapy.core.scraper slot handling (issue 271)
  • Added Item.copy (issue 290)
  • Collect idle downloader slots (issue 297)
  • Add ftp:// scheme downloader handler (issue 329)
  • Added downloader benchmark webserver and spider tools Benchmarking
  • Moved persistent (on disk) queues to a separate project (queuelib) which scrapy now depends on
  • Add scrapy commands using external libraries (issue 260)
  • Added --pdb option to scrapy command line tool
  • Added XPathSelector.remove_namespaces() which allows to remove all namespaces from XML documents for convenience (to work with namespace-less XPaths). Documented in Selectors.
  • Several improvements to spider contracts
  • New default middleware named MetaRefreshMiddldeware that handles meta-refresh html tag redirections,
  • MetaRefreshMiddldeware and RedirectMiddleware have different priorities to address #62
  • added from_crawler method to spiders
  • added system tests with mock server
  • more improvements to Mac OS compatibility (thanks Alex Cepoi)
  • several more cleanups to singletons and multi-spider support (thanks Nicolas Ramirez)
  • support custom download slots
  • added –spider option to “shell” command.
  • log overridden settings when scrapy starts

Thanks to everyone who contribute to this release. Here is a list of contributors sorted by number of commits:

130 Pablo Hoffman <pablo@...>
 97 Daniel Graña <dangra@...>
 20 Nicolás Ramírez <nramirez.uy@...>
 13 Mikhail Korobov <kmike84@...>
 12 Pedro Faustino <pedrobandim@...>
 11 Steven Almeroth <sroth77@...>
  5 Rolando Espinoza La fuente <darkrho@...>
  4 Michal Danilak <mimino.coder@...>
  4 Alex Cepoi <alex.cepoi@...>
  4 Alexandr N Zamaraev (aka tonal) <tonal@...>
  3 paul <paul.tremberth@...>
  3 Martin Olveyra <molveyra@...>
  3 Jordi Llonch <llonchj@...>
  3 arijitchakraborty <myself.arijit@...>
  2 Shane Evans <shane.evans@...>
  2 joehillen <joehillen@...>
  2 Hart <HartSimha@...>
  2 Dan <ellisd23@...>
  1 Zuhao Wan <wanzuhao@...>
  1 whodatninja <blake@...>
  1 vkrest <v.krestiannykov@...>
  1 tpeng <pengtaoo@...>
  1 Tom Mortimer-Jones <tom@...>
  1 Rocio Aramberri <roschegel@...>
  1 Pedro <pedro@...>
  1 notsobad <wangxiaohugg@...>
  1 Natan L <kuyanatan.nlao@...>
  1 Mark Grey <mark.grey@...>
  1 Luan <luanpab@...>
  1 Libor Nenadál <libor.nenadal@...>
  1 Juan M Uys <opyate@...>
  1 Jonas Brunsgaard <jonas.brunsgaard@...>
  1 Ilya Baryshev <baryshev@...>
  1 Hasnain Lakhani <m.hasnain.lakhani@...>
  1 Emanuel Schorsch <emschorsch@...>
  1 Chris Tilden <chris.tilden@...>
  1 Capi Etheriel <barraponto@...>
  1 cacovsky <amarquesferraz@...>
  1 Berend Iwema <berend@...>

0.16.5 (released 2013-05-30)

  • obey request method when scrapy deploy is redirected to a new endpoint (commit 8c4fcee)
  • fix inaccurate downloader middleware documentation. refs #280 (commit 40667cb)
  • doc: remove links to diveintopython.org, which is no longer available. closes #246 (commit bd58bfa)
  • Find form nodes in invalid html5 documents (commit e3d6945)
  • Fix typo labeling attrs type bool instead of list (commit a274276)

0.16.4 (released 2013-01-23)

  • fixes spelling errors in documentation (commit 6d2b3aa)
  • add doc about disabling an extension. refs #132 (commit c90de33)
  • Fixed error message formatting. log.err() doesn’t support cool formatting and when error occurred, the message was: “ERROR: Error processing %(item)s” (commit c16150c)
  • lint and improve images pipeline error logging (commit 56b45fc)
  • fixed doc typos (commit 243be84)
  • add documentation topics: Broad Crawls & Common Practies (commit 1fbb715)
  • fix bug in scrapy parse command when spider is not specified explicitly. closes #209 (commit c72e682)
  • Update docs/topics/commands.rst (commit 28eac7a)

0.16.3 (released 2012-12-07)

0.16.2 (released 2012-11-09)

0.16.1 (released 2012-10-26)

  • fixed LogStats extension, which got broken after a wrong merge before the 0.16 release (commit 8c780fd)
  • better backwards compatibility for scrapy.conf.settings (commit 3403089)
  • extended documentation on how to access crawler stats from extensions (commit c4da0b5)
  • removed .hgtags (no longer needed now that scrapy uses git) (commit d52c188)
  • fix dashes under rst headers (commit fa4f7f9)
  • set release date for 0.16.0 in news (commit e292246)

0.16.0 (released 2012-10-18)

Scrapy changes:

  • added Spiders Contracts, a mechanism for testing spiders in a formal/reproducible way
  • added options -o and -t to the runspider command
  • documented AutoThrottle extension and added to extensions installed by default. You still need to enable it with AUTOTHROTTLE_ENABLED
  • major Stats Collection refactoring: removed separation of global/per-spider stats, removed stats-related signals (stats_spider_opened, etc). Stats are much simpler now, backwards compatibility is kept on the Stats Collector API and signals.
  • added process_start_requests() method to spider middlewares
  • dropped Signals singleton. Signals should now be accesed through the Crawler.signals attribute. See the signals documentation for more info.
  • dropped Signals singleton. Signals should now be accesed through the Crawler.signals attribute. See the signals documentation for more info.
  • dropped Stats Collector singleton. Stats can now be accessed through the Crawler.stats attribute. See the stats collection documentation for more info.
  • documented Core API
  • lxml is now the default selectors backend instead of libxml2
  • ported FormRequest.from_response() to use lxml instead of ClientForm
  • removed modules: scrapy.xlib.BeautifulSoup and scrapy.xlib.ClientForm
  • SitemapSpider: added support for sitemap urls ending in .xml and .xml.gz, even if they advertise a wrong content type (commit 10ed28b)
  • StackTraceDump extension: also dump trackref live references (commit fe2ce93)
  • nested items now fully supported in JSON and JSONLines exporters
  • added cookiejar Request meta key to support multiple cookie sessions per spider
  • decoupled encoding detection code to w3lib.encoding, and ported Scrapy code to use that module
  • dropped support for Python 2.5. See http://blog.scrapinghub.com/2012/02/27/scrapy-0-15-dropping-support-for-python-2-5/
  • dropped support for Twisted 2.5
  • added REFERER_ENABLED setting, to control referer middleware
  • changed default user agent to: Scrapy/VERSION (+http://scrapy.org)
  • removed (undocumented) HTMLImageLinkExtractor class from scrapy.contrib.linkextractors.image
  • removed per-spider settings (to be replaced by instantiating multiple crawler objects)
  • USER_AGENT spider attribute will no longer work, use user_agent attribute instead
  • DOWNLOAD_TIMEOUT spider attribute will no longer work, use download_timeout attribute instead
  • removed ENCODING_ALIASES setting, as encoding auto-detection has been moved to the w3lib library
  • promoted DjangoItem to main contrib
  • LogFormatter method now return dicts(instead of strings) to support lazy formatting (issue 164, commit dcef7b0)
  • downloader handlers (DOWNLOAD_HANDLERS setting) now receive settings as the first argument of the constructor
  • replaced memory usage acounting with (more portable) resource module, removed scrapy.utils.memory module
  • removed signal: scrapy.mail.mail_sent
  • removed TRACK_REFS setting, now trackrefs is always enabled
  • DBM is now the default storage backend for HTTP cache middleware
  • number of log messages (per level) are now tracked through Scrapy stats (stat name: log_count/LEVEL)
  • number received responses are now tracked through Scrapy stats (stat name: response_received_count)
  • removed scrapy.log.started attribute

0.14.4

0.14.3

  • forgot to include pydispatch license. #118 (commit fd85f9c)
  • include egg files used by testsuite in source distribution. #118 (commit c897793)
  • update docstring in project template to avoid confusion with genspider command, which may be considered as an advanced feature. refs #107 (commit 2548dcc)
  • added note to docs/topics/firebug.rst about google directory being shut down (commit 668e352)
  • dont discard slot when empty, just save in another dict in order to recycle if needed again. (commit 8e9f607)
  • do not fail handling unicode xpaths in libxml2 backed selectors (commit b830e95)
  • fixed minor mistake in Request objects documentation (commit bf3c9ee)
  • fixed minor defect in link extractors documentation (commit ba14f38)
  • removed some obsolete remaining code related to sqlite support in scrapy (commit 0665175)

0.14.2

  • move buffer pointing to start of file before computing checksum. refs #92 (commit 6a5bef2)
  • Compute image checksum before persisting images. closes #92 (commit 9817df1)
  • remove leaking references in cached failures (commit 673a120)
  • fixed bug in MemoryUsage extension: get_engine_status() takes exactly 1 argument (0 given) (commit 11133e9)
  • fixed struct.error on http compression middleware. closes #87 (commit 1423140)
  • ajax crawling wasn’t expanding for unicode urls (commit 0de3fb4)
  • Catch start_requests iterator errors. refs #83 (commit 454a21d)
  • Speed-up libxml2 XPathSelector (commit 2fbd662)
  • updated versioning doc according to recent changes (commit 0a070f5)
  • scrapyd: fixed documentation link (commit 2b4e4c3)
  • extras/makedeb.py: no longer obtaining version from git (commit caffe0e)

0.14.1

  • extras/makedeb.py: no longer obtaining version from git (commit caffe0e)
  • bumped version to 0.14.1 (commit 6cb9e1c)
  • fixed reference to tutorial directory (commit 4b86bd6)
  • doc: removed duplicated callback argument from Request.replace() (commit 1aeccdd)
  • fixed formatting of scrapyd doc (commit 8bf19e6)
  • Dump stacks for all running threads and fix engine status dumped by StackTraceDump extension (commit 14a8e6e)
  • added comment about why we disable ssl on boto images upload (commit 5223575)
  • SSL handshaking hangs when doing too many parallel connections to S3 (commit 63d583d)
  • change tutorial to follow changes on dmoz site (commit bcb3198)
  • Avoid _disconnectedDeferred AttributeError exception in Twisted>=11.1.0 (commit 98f3f87)
  • allow spider to set autothrottle max concurrency (commit 175a4b5)

0.14

New features and settings
  • Support for AJAX crawleable urls
  • New persistent scheduler that stores requests on disk, allowing to suspend and resume crawls (r2737)
  • added -o option to scrapy crawl, a shortcut for dumping scraped items into a file (or standard output using -)
  • Added support for passing custom settings to Scrapyd schedule.json api (r2779, r2783)
  • New ChunkedTransferMiddleware (enabled by default) to support chunked transfer encoding (r2769)
  • Add boto 2.0 support for S3 downloader handler (r2763)
  • Added marshal to formats supported by feed exports (r2744)
  • In request errbacks, offending requests are now received in failure.request attribute (r2738)
  • Big downloader refactoring to support per domain/ip concurrency limits (r2732)
  • Added builtin caching DNS resolver (r2728)
  • Moved Amazon AWS-related components/extensions (SQS spider queue, SimpleDB stats collector) to a separate project: [scaws](https://github.com/scrapinghub/scaws) (r2706, r2714)
  • Moved spider queues to scrapyd: scrapy.spiderqueue -> scrapyd.spiderqueue (r2708)
  • Moved sqlite utils to scrapyd: scrapy.utils.sqlite -> scrapyd.sqlite (r2781)
  • Real support for returning iterators on start_requests() method. The iterator is now consumed during the crawl when the spider is getting idle (r2704)
  • Added REDIRECT_ENABLED setting to quickly enable/disable the redirect middleware (r2697)
  • Added RETRY_ENABLED setting to quickly enable/disable the retry middleware (r2694)
  • Added CloseSpider exception to manually close spiders (r2691)
  • Improved encoding detection by adding support for HTML5 meta charset declaration (r2690)
  • Refactored close spider behavior to wait for all downloads to finish and be processed by spiders, before closing the spider (r2688)
  • Added SitemapSpider (see documentation in Spiders page) (r2658)
  • Added LogStats extension for periodically logging basic stats (like crawled pages and scraped items) (r2657)
  • Make handling of gzipped responses more robust (#319, r2643). Now Scrapy will try and decompress as much as possible from a gzipped response, instead of failing with an IOError.
  • Simplified !MemoryDebugger extension to use stats for dumping memory debugging info (r2639)
  • Added new command to edit spiders: scrapy edit (r2636) and -e flag to genspider command that uses it (r2653)
  • Changed default representation of items to pretty-printed dicts. (r2631). This improves default logging by making log more readable in the default case, for both Scraped and Dropped lines.
  • Added spider_error signal (r2628)
  • Added COOKIES_ENABLED setting (r2625)
  • Stats are now dumped to Scrapy log (default value of STATS_DUMP setting has been changed to True). This is to make Scrapy users more aware of Scrapy stats and the data that is collected there.
  • Added support for dynamically adjusting download delay and maximum concurrent requests (r2599)
  • Added new DBM HTTP cache storage backend (r2576)
  • Added listjobs.json API to Scrapyd (r2571)
  • CsvItemExporter: added join_multivalued parameter (r2578)
  • Added namespace support to xmliter_lxml (r2552)
  • Improved cookies middleware by making COOKIES_DEBUG nicer and documenting it (r2579)
  • Several improvements to Scrapyd and Link extractors
Code rearranged and removed
  • Merged item passed and item scraped concepts, as they have often proved confusing in the past. This means: (r2630)
    • original item_scraped signal was removed
    • original item_passed signal was renamed to item_scraped
    • old log lines Scraped Item... were removed
    • old log lines Passed Item... were renamed to Scraped Item... lines and downgraded to DEBUG level
  • Reduced Scrapy codebase by striping part of Scrapy code into two new libraries:
    • w3lib (several functions from scrapy.utils.{http,markup,multipart,response,url}, done in r2584)
    • scrapely (was scrapy.contrib.ibl, done in r2586)
  • Removed unused function: scrapy.utils.request.request_info() (r2577)
  • Removed googledir project from examples/googledir. There’s now a new example project called dirbot available on github: https://github.com/scrapy/dirbot
  • Removed support for default field values in Scrapy items (r2616)
  • Removed experimental crawlspider v2 (r2632)
  • Removed scheduler middleware to simplify architecture. Duplicates filter is now done in the scheduler itself, using the same dupe fltering class as before (DUPEFILTER_CLASS setting) (r2640)
  • Removed support for passing urls to scrapy crawl command (use scrapy parse instead) (r2704)
  • Removed deprecated Execution Queue (r2704)
  • Removed (undocumented) spider context extension (from scrapy.contrib.spidercontext) (r2780)
  • removed CONCURRENT_SPIDERS setting (use scrapyd maxproc instead) (r2789)
  • Renamed attributes of core components: downloader.sites -> downloader.slots, scraper.sites -> scraper.slots (r2717, r2718)
  • Renamed setting CLOSESPIDER_ITEMPASSED to CLOSESPIDER_ITEMCOUNT (r2655). Backwards compatibility kept.

0.12

The numbers like #NNN reference tickets in the old issue tracker (Trac) which is no longer available.

New features and improvements
  • Passed item is now sent in the item argument of the item_passed (#273)
  • Added verbose option to scrapy version command, useful for bug reports (#298)
  • HTTP cache now stored by default in the project data dir (#279)
  • Added project data storage directory (#276, #277)
  • Documented file structure of Scrapy projects (see command-line tool doc)
  • New lxml backend for XPath selectors (#147)
  • Per-spider settings (#245)
  • Support exit codes to signal errors in Scrapy commands (#248)
  • Added -c argument to scrapy shell command
  • Made libxml2 optional (#260)
  • New deploy command (#261)
  • Added CLOSESPIDER_PAGECOUNT setting (#253)
  • Added CLOSESPIDER_ERRORCOUNT setting (#254)
Scrapyd changes
  • Scrapyd now uses one process per spider
  • It stores one log file per spider run, and rotate them keeping the lastest 5 logs per spider (by default)
  • A minimal web ui was added, available at http://localhost:6800 by default
  • There is now a scrapy server command to start a Scrapyd server of the current project
Changes to settings
  • added HTTPCACHE_ENABLED setting (False by default) to enable HTTP cache middleware
  • changed HTTPCACHE_EXPIRATION_SECS semantics: now zero means “never expire”.
Deprecated/obsoleted functionality
  • Deprecated runserver command in favor of server command which starts a Scrapyd server. See also: Scrapyd changes
  • Deprecated queue command in favor of using Scrapyd schedule.json API. See also: Scrapyd changes
  • Removed the !LxmlItemLoader (experimental contrib which never graduated to main contrib)

0.10

The numbers like #NNN reference tickets in the old issue tracker (Trac) which is no longer available.

New features and improvements
  • New Scrapy service called scrapyd for deploying Scrapy crawlers in production (#218) (documentation available)
  • Simplified Images pipeline usage which doesn’t require subclassing your own images pipeline now (#217)
  • Scrapy shell now shows the Scrapy log by default (#206)
  • Refactored execution queue in a common base code and pluggable backends called “spider queues” (#220)
  • New persistent spider queue (based on SQLite) (#198), available by default, which allows to start Scrapy in server mode and then schedule spiders to run.
  • Added documentation for Scrapy command-line tool and all its available sub-commands. (documentation available)
  • Feed exporters with pluggable backends (#197) (documentation available)
  • Deferred signals (#193)
  • Added two new methods to item pipeline open_spider(), close_spider() with deferred support (#195)
  • Support for overriding default request headers per spider (#181)
  • Replaced default Spider Manager with one with similar functionality but not depending on Twisted Plugins (#186)
  • Splitted Debian package into two packages - the library and the service (#187)
  • Scrapy log refactoring (#188)
  • New extension for keeping persistent spider contexts among different runs (#203)
  • Added dont_redirect request.meta key for avoiding redirects (#233)
  • Added dont_retry request.meta key for avoiding retries (#234)
Command-line tool changes
  • New scrapy command which replaces the old scrapy-ctl.py (#199) - there is only one global scrapy command now, instead of one scrapy-ctl.py per project - Added scrapy.bat script for running more conveniently from Windows
  • Added bash completion to command-line tool (#210)
  • Renamed command start to runserver (#209)
API changes
  • url and body attributes of Request objects are now read-only (#230)
  • Request.copy() and Request.replace() now also copies their callback and errback attributes (#231)
  • Removed UrlFilterMiddleware from scrapy.contrib (already disabled by default)
  • Offsite middelware doesn’t filter out any request coming from a spider that doesn’t have a allowed_domains attribute (#225)
  • Removed Spider Manager load() method. Now spiders are loaded in the constructor itself.
  • Changes to Scrapy Manager (now called “Crawler”):
    • scrapy.core.manager.ScrapyManager class renamed to scrapy.crawler.Crawler
    • scrapy.core.manager.scrapymanager singleton moved to scrapy.project.crawler
  • Moved module: scrapy.contrib.spidermanager to scrapy.spidermanager
  • Spider Manager singleton moved from scrapy.spider.spiders to the spiders` attribute of ``scrapy.project.crawler singleton.
  • moved Stats Collector classes: (#204)
    • scrapy.stats.collector.StatsCollector to scrapy.statscol.StatsCollector
    • scrapy.stats.collector.SimpledbStatsCollector to scrapy.contrib.statscol.SimpledbStatsCollector
  • default per-command settings are now specified in the default_settings attribute of command object class (#201)
  • changed arguments of Item pipeline process_item() method from (spider, item) to (item, spider)
    • backwards compatibility kept (with deprecation warning)
  • moved scrapy.core.signals module to scrapy.signals
    • backwards compatibility kept (with deprecation warning)
  • moved scrapy.core.exceptions module to scrapy.exceptions
    • backwards compatibility kept (with deprecation warning)
  • added handles_request() class method to BaseSpider
  • dropped scrapy.log.exc() function (use scrapy.log.err() instead)
  • dropped component argument of scrapy.log.msg() function
  • dropped scrapy.log.log_level attribute
  • Added from_settings() class methods to Spider Manager, and Item Pipeline Manager
Changes to settings
  • Added HTTPCACHE_IGNORE_SCHEMES setting to ignore certain schemes on !HttpCacheMiddleware (#225)
  • Added SPIDER_QUEUE_CLASS setting which defines the spider queue to use (#220)
  • Added KEEP_ALIVE setting (#220)
  • Removed SERVICE_QUEUE setting (#220)
  • Removed COMMANDS_SETTINGS_MODULE setting (#201)
  • Renamed REQUEST_HANDLERS to DOWNLOAD_HANDLERS and make download handlers classes (instead of functions)

0.9

The numbers like #NNN reference tickets in the old issue tracker (Trac) which is no longer available.

New features and improvements
  • Added SMTP-AUTH support to scrapy.mail
  • New settings added: MAIL_USER, MAIL_PASS (r2065 | #149)
  • Added new scrapy-ctl view command - To view URL in the browser, as seen by Scrapy (r2039)
  • Added web service for controlling Scrapy process (this also deprecates the web console. (r2053 | #167)
  • Support for running Scrapy as a service, for production systems (r1988, r2054, r2055, r2056, r2057 | #168)
  • Added wrapper induction library (documentation only available in source code for now). (r2011)
  • Simplified and improved response encoding support (r1961, r1969)
  • Added LOG_ENCODING setting (r1956, documentation available)
  • Added RANDOMIZE_DOWNLOAD_DELAY setting (enabled by default) (r1923, doc available)
  • MailSender is no longer IO-blocking (r1955 | #146)
  • Linkextractors and new Crawlspider now handle relative base tag urls (r1960 | #148)
  • Several improvements to Item Loaders and processors (r2022, r2023, r2024, r2025, r2026, r2027, r2028, r2029, r2030)
  • Added support for adding variables to telnet console (r2047 | #165)
  • Support for requests without callbacks (r2050 | #166)
API changes
  • Change Spider.domain_name to Spider.name (SEP-012, r1975)
  • Response.encoding is now the detected encoding (r1961)
  • HttpErrorMiddleware now returns None or raises an exception (r2006 | #157)
  • scrapy.command modules relocation (r2035, r2036, r2037)
  • Added ExecutionQueue for feeding spiders to scrape (r2034)
  • Removed ExecutionEngine singleton (r2039)
  • Ported S3ImagesStore (images pipeline) to use boto and threads (r2033)
  • Moved module: scrapy.management.telnet to scrapy.telnet (r2047)
Changes to default settings
  • Changed default SCHEDULER_ORDER to DFO (r1939)

0.8

The numbers like #NNN reference tickets in the old issue tracker (Trac) which is no longer available.

New features
  • Added DEFAULT_RESPONSE_ENCODING setting (r1809)
  • Added dont_click argument to FormRequest.from_response() method (r1813, r1816)
  • Added clickdata argument to FormRequest.from_response() method (r1802, r1803)
  • Added support for HTTP proxies (HttpProxyMiddleware) (r1781, r1785)
  • Offsite spider middleware now logs messages when filtering out requests (r1841)
Backwards-incompatible changes
  • Changed scrapy.utils.response.get_meta_refresh() signature (r1804)
  • Removed deprecated scrapy.item.ScrapedItem class - use scrapy.item.Item instead (r1838)
  • Removed deprecated scrapy.xpath module - use scrapy.selector instead. (r1836)
  • Removed deprecated core.signals.domain_open signal - use core.signals.domain_opened instead (r1822)
  • log.msg() now receives a spider argument (r1822)
    • Old domain argument has been deprecated and will be removed in 0.9. For spiders, you should always use the spider argument and pass spider references. If you really want to pass a string, use the component argument instead.
  • Changed core signals domain_opened, domain_closed, domain_idle
  • Changed Item pipeline to use spiders instead of domains
    • The domain argument of process_item() item pipeline method was changed to spider, the new signature is: process_item(spider, item) (r1827 | #105)
    • To quickly port your code (to work with Scrapy 0.8) just use spider.domain_name where you previously used domain.
  • Changed Stats API to use spiders instead of domains (r1849 | #113)
    • StatsCollector was changed to receive spider references (instead of domains) in its methods (set_value, inc_value, etc).
    • added StatsCollector.iter_spider_stats() method
    • removed StatsCollector.list_domains() method
    • Also, Stats signals were renamed and now pass around spider references (instead of domains). Here’s a summary of the changes:
    • To quickly port your code (to work with Scrapy 0.8) just use spider.domain_name where you previously used domain. spider_stats contains exactly the same data as domain_stats.
  • CloseDomain extension moved to scrapy.contrib.closespider.CloseSpider (r1833)
    • Its settings were also renamed:
      • CLOSEDOMAIN_TIMEOUT to CLOSESPIDER_TIMEOUT
      • CLOSEDOMAIN_ITEMCOUNT to CLOSESPIDER_ITEMCOUNT
  • Removed deprecated SCRAPYSETTINGS_MODULE environment variable - use SCRAPY_SETTINGS_MODULE instead (r1840)
  • Renamed setting: REQUESTS_PER_DOMAIN to CONCURRENT_REQUESTS_PER_SPIDER (r1830, r1844)
  • Renamed setting: CONCURRENT_DOMAINS to CONCURRENT_SPIDERS (r1830)
  • Refactored HTTP Cache middleware
  • HTTP Cache middleware has been heavilty refactored, retaining the same functionality except for the domain sectorization which was removed. (r1843 )
  • Renamed exception: DontCloseDomain to DontCloseSpider (r1859 | #120)
  • Renamed extension: DelayedCloseDomain to SpiderCloseDelay (r1861 | #121)
  • Removed obsolete scrapy.utils.markup.remove_escape_chars function - use scrapy.utils.markup.replace_escape_chars instead (r1865)

0.7

First release of Scrapy.

Contributing to Scrapy

Important

Double check you are reading the most recent version of this document at http://doc.scrapy.org/en/master/contributing.html

There are many ways to contribute to Scrapy. Here are some of them:

  • Blog about Scrapy. Tell the world how you’re using Scrapy. This will help newcomers with more examples and the Scrapy project to increase its visibility.
  • Report bugs and request features in the issue tracker, trying to follow the guidelines detailed in Reporting bugs below.
  • Submit patches for new functionality and/or bug fixes. Please read Writing patches and Submitting patches below for details on how to write and submit a patch.
  • Join the scrapy-users mailing list and share your ideas on how to improve Scrapy. We’re always open to suggestions.

Reporting bugs

Note

Please report security issues only to scrapy-security@googlegroups.com. This is a private list only open to trusted Scrapy developers, and its archives are not public.

Well-written bug reports are very helpful, so keep in mind the following guidelines when reporting a new bug.

  • check the FAQ first to see if your issue is addressed in a well-known question
  • check the open issues to see if it has already been reported. If it has, don’t dismiss the report but check the ticket history and comments, you may find additional useful information to contribute.
  • search the scrapy-users list to see if it has been discussed there, or if you’re not sure if what you’re seeing is a bug. You can also ask in the #scrapy IRC channel.
  • write complete, reproducible, specific bug reports. The smaller the test case, the better. Remember that other developers won’t have your project to reproduce the bug, so please include all relevant files required to reproduce it.
  • include the output of scrapy version -v so developers working on your bug know exactly which version and platform it occurred on, which is often very helpful for reproducing it, or knowing if it was already fixed.

Writing patches

The better written a patch is, the higher chance that it’ll get accepted and the sooner that will be merged.

Well-written patches should:

  • contain the minimum amount of code required for the specific change. Small patches are easier to review and merge. So, if you’re doing more than one change (or bug fix), please consider submitting one patch per change. Do not collapse multiple changes into a single patch. For big changes consider using a patch queue.
  • pass all unit-tests. See Running tests below.
  • include one (or more) test cases that check the bug fixed or the new functionality added. See Writing tests below.
  • if you’re adding or changing a public (documented) API, please include the documentation changes in the same patch. See Documentation policies below.

Submitting patches

The best way to submit a patch is to issue a pull request on Github, optionally creating a new issue first.

Remember to explain what was fixed or the new functionality (what it is, why it’s needed, etc). The more info you include, the easier will be for core developers to understand and accept your patch.

You can also discuss the new functionality (or bug fix) before creating the patch, but it’s always good to have a patch ready to illustrate your arguments and show that you have put some additional thought into the subject. A good starting point is to send a pull request on Github. It can be simple enough to illustrate your idea, and leave documentation/tests for later, after the idea has been validated and proven useful. Alternatively, you can send an email to scrapy-users to discuss your idea first.

Finally, try to keep aesthetic changes (PEP 8 compliance, unused imports removal, etc) in separate commits than functional changes. This will make pull requests easier to review and more likely to get merged.

Coding style

Please follow these coding conventions when writing code for inclusion in Scrapy:

  • Unless otherwise specified, follow PEP 8.
  • It’s OK to use lines longer than 80 chars if it improves the code readability.
  • Don’t put your name in the code you contribute. Our policy is to keep the contributor’s name in the AUTHORS file distributed with Scrapy.

Scrapy Contrib

Scrapy contrib shares a similar rationale as Django contrib, which is explained in this post. If you are working on a new functionality, please follow that rationale to decide whether it should be a Scrapy contrib. If unsure, you can ask in scrapy-users.

Documentation policies

  • Don’t use docstrings for documenting classes, or methods which are already documented in the official (sphinx) documentation. For example, the ItemLoader.add_value() method should be documented in the sphinx documentation, not its docstring.
  • Do use docstrings for documenting functions not present in the official (sphinx) documentation, such as functions from scrapy.utils package and its sub-modules.

Tests

Tests are implemented using the Twisted unit-testing framework, running tests requires tox.

Running tests

Make sure you have a recent enough tox installation:

tox --version

If your version is older than 1.7.0, please update it first:

pip install -U tox

To run all tests go to the root directory of Scrapy source code and run:

tox

To run a specific test (say tests/test_loader.py) use:

tox -- tests/test_loader.py
Writing tests

All functionality (including new features and bug fixes) must include a test case to check that it works as expected, so please include tests for your patches if you want them to get accepted sooner.

Scrapy uses unit-tests, which are located in the tests/ directory. Their module name typically resembles the full path of the module they’re testing. For example, the item loaders code is in:

scrapy.loader

And their unit-tests are in:

tests/test_loader.py

Versioning and API Stability

Versioning

Scrapy uses the odd-numbered versions for development releases.

There are 3 numbers in a Scrapy version: A.B.C

  • A is the major version. This will rarely change and will signify very large changes.
  • B is the release number. This will include many changes including features and things that possibly break backwards compatibility. Even Bs will be stable branches, and odd Bs will be development.
  • C is the bugfix release number.

For example:

  • 0.14.1 is the first bugfix release of the 0.14 series (safe to use in production)

API Stability

API stability was one of the major goals for the 1.0 release.

Methods or functions that start with a single dash (_) are private and should never be relied as stable.

Also, keep in mind that stable doesn’t mean complete: stable APIs could grow new methods or functionality but the existing methods should keep working the same way.

Release notes
See what has changed in recent Scrapy versions.
Contributing to Scrapy
Learn how to contribute to the Scrapy project.
Versioning and API Stability
Understand Scrapy versioning and API stability.